TY - JOUR AU - Fei, Baowei AU - Wang, Bo AU - Bian, ZhengZhoag AU - Cheng, Jingzhi PY - 1993 SP - 006 ST - The effect of the delay distribution in phased-array ultrasonic diagnostic imaging T2 - Technical Acoustics TI - The effect of the delay distribution in phased-array ultrasonic diagnostic imaging VL - 1 ID - 272 ER - TY - JOUR AU - Ni, Xiaoke AU - Fei, Baowei AU - Cheng, Jingzhi PY - 1996 SP - 005 ST - ECG Synchronization in Pulse Oximetry [J] T2 - Journal of Biomedical Engineering TI - ECG Synchronization in Pulse Oximetry [J] VL - 4 ID - 273 ER - TY - JOUR AU - Fei, Baowei AU - Zhuang, Tiange PY - 1997 SP - 006 ST - The study of a frameless stereotatic localization method using DSA T2 - Chinese Journal of Medical Instrumentation TI - The study of a frameless stereotatic localization method using DSA VL - 4 ID - 277 ER - TY - JOUR AU - Fei, Baowei AU - Zhuang, Tiange IS - 1 PY - 1998 SP - 49-54 ST - The development and application of medical robotics and computer assisted surgery T2 - Beijing Biomedical Engineering TI - The development and application of medical robotics and computer assisted surgery VL - 17 ID - 258 ER - TY - JOUR AU - Fei, Baowei AU - Zhuang, Tiange PY - 1998 SP - 023 ST - The Method and Development of Computer Assisted Surgery [J] T2 - JOURNAL OF BIOMEDICAL ENGINEERING TI - The Method and Development of Computer Assisted Surgery [J] VL - 2 ID - 264 ER - TY - JOUR AU - Fei, B AU - Zhuang, T AU - Hu, J PY - 1998 SN - 1006-2467 SP - 130-133 ST - Frameless Intracranial Localization Method Based on Digital Subtraction Angiography T2 - JOURNAL-SHANGHAI JIAOTONG UNIVERSITY-CHINESE EDITION- TI - Frameless Intracranial Localization Method Based on Digital Subtraction Angiography VL - 32 ID - 146 ER - TY - JOUR AB - In this paper, we put forward a systematic method to analyze, control and evaluate the safety issues of medical robotics. We created a safety model that consists of three axes to analyze safety factors. Software and hardware are the two material axes. The third axis is the policy that controls all phases of design, production, testing and application of the robot system. The policy was defined as hazard identification and safety insurance control (HISIC) that includes seven principles: definitions and requirements, hazard identification, safety insurance control, safety critical limits, monitoring and control, verification and validation, system log and documentation. HISIC was implemented in the development of a robot for urological applications that was known as URObot. The URObot is a universal robot with different modules adaptable for 3D ultrasound image-guided interstitial laser coagulation, radiation seed implantation, laser resection, and electrical resection of the prostate. Safety was always the key issue in the building of the robot. The HISIC strategies were adopted for safety enhancement in mechanical, electrical and software design. The initial test on URObot showed that HISIC had the potential ability to improve the safety of the system. Further safety experiments are being conducted in our laboratory. (C) 2001 Elsevier Science Ltd. All rights reserved. AN - WOS:000170273500008 AU - Fei, B. W. AU - Ng, W. S. AU - Chauhan, S. AU - Kwoh, C. K. DA - Aug DO - 10.1016/s0951-8320(01)00037-0 IS - 2 N1 - Times Cited: 18 Chauhan, Sunita/A-3814-2011; Fei, Baowei /E-6898-2014 Fei, Baowei /0000-0002-9123-9484 0 21 PY - 2001 SN - 0951-8320 SP - 183-192 ST - The safety issues of medical robotics T2 - Reliability Engineering & System Safety TI - The safety issues of medical robotics UR - ://WOS:000170273500008 VL - 73 ID - 291 ER - TY - JOUR AB - The goal of this research is to register real-time interventional magnetic resonance imaging (iMRI) slice images with a previously obtained high-resolution MRI image volume, which in turn can be registered with functional images such as those from SPECT. The immediate application is in iMRI-guided treatment of prostate cancer, where additional images are desired to improve tumor targeting. In this article, simulation experiments are performed to demonstrate the feasibility of slice-to-volume registration for this application. We acquired 3D volume images from a 1.5-T MRI system and simulated low-field iMRI image slices by creating thick slices and adding noise. We created a slice-to-volume mutual information registration algorithm with special features to improve robustness. Features included a multiresolution approach, two similarity measures, and automatic restarting to avoid local minima. To assess the quality of registration, we calculated 3D displacements on a voxel-by-voxel basis over a volume of interest between slice-to-volume registration and volume-to-volume registration, which was previously shown to be quite accurate. More than 800 registration experiments were performed on MR images of three volunteers. The slice-to-volume registration algorithm was very robust and accurate for transverse slice images covering the prostate, with a registration error of only 0.4 +/- 0.2 mm. Error was greater at other slice orientations and positions. The automatic slice-to-volume mutual information registration algorithm is robust and probably sufficiently accurate to aid in iMRI-guided treatment of prostate cancer. AD - Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA. AN - 12582978 AU - Fei, B. AU - Duerk, J. L. AU - Wilson, D. L. DO - 10.1002/igs.10052 [doi] DP - Nlm ET - 2003/02/13 IS - 5 KW - Algorithms Computer Simulation Feasibility Studies Humans Imaging, Three-Dimensional/ methods Magnetic Resonance Imaging/ methods Male Minimally Invasive Surgical Procedures Prostatic Neoplasms/ pathology L1 - internal-pdf://1458834867/Fei-2002-Automatic 3D registration for interve.pdf LA - eng N1 - Fei, Baowei Duerk, Jeffrey L Wilson, David L R01 CA084433/CA/NCI NIH HHS/United States R01-CA84433-01/CA/NCI NIH HHS/United States R33-CA88144-01/CA/NCI NIH HHS/United States Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. England Computer aided surgery : official journal of the International Society for Computer Aided Surgery Comput Aided Surg. 2002;7(5):257-67. PY - 2002 SN - 1092-9088 (Print) 1092-9088 (Linking) SP - 257-67 ST - Automatic 3D registration for interventional MRI-guided treatment of prostate cancer T2 - Comput Aided Surg TI - Automatic 3D registration for interventional MRI-guided treatment of prostate cancer UR - http://www.tandfonline.com/doi/pdf/10.3109/10929080209146034?needAccess=true VL - 7 ID - 93 ER - TY - JOUR AB - A three-dimensional (3D) mutual information registration method was created and used to register MRI volumes of the pelvis and prostate. It had special features to improve robustness. First, it used a multi-resolution approach and performed registration from low to high resolution. Second, it used two similarity measures, correlation coefficient at lower resolutions and mutual information at full resolution, because of their particular advantages. Third, we created a method to avoid local minima by restarting the registration with randomly perturbed parameters. The criterion for restarting was a correlation coefficient below an empirically determined threshold. Experiments determined the accuracy of registration under conditions found in potential applications in prostate cancer diagnosis, staging, treatment and interventional MRI (iMRI) guided therapies. Images were acquired in the diagnostic (supine) and treatment position (supine with legs raised). Images were also acquired as a function of bladder filling and the time interval between imaging sessions. Overall studies on three patients and three healthy volunteers, when both volumes in a pair were obtained in the diagnostic position under comparable conditions, bony landmarks and prostate 3D centroids were aligned within 1.6 +/- 0.2 mm and 1.4 +/- 0.2 mm, respectively, values only slightly larger than a voxel. Analysis suggests that actual errors are smaller because of the uncertainty in landmark localization and prostate segmentation. Between the diagnostic and treatment positions, bony landmarks continued to register well, but prostate centroids moved towards the posterior 2.8-3.4 mm. Manual cropping to remove voxels in the legs was necessary to register these images. In conclusion, automatic, rigid body registration is probably sufficiently accurate for many applications in prostate cancer. For potential iMRI-guided treatments, the small prostate displacement between the diagnostic and treatment positions can probably be avoided by acquiring volumes in similar positions and by reducing bladder and rectal volumes. AD - Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA. AN - 11931473 AU - Fei, B. AU - Wheaton, A. AU - Lee, Z. AU - Duerk, J. L. AU - Wilson, D. L. DA - Mar 07 DP - Nlm ET - 2002/04/05 IS - 5 KW - Algorithms Humans Magnetic Resonance Imaging/ methods Male Models, Statistical Pelvis/ pathology Prostate/ pathology Prostatic Neoplasms/ therapy Radiometry/ methods LA - eng N1 - Fei, Baowei Wheaton, Andrew Lee, Zhenghong Duerk, Jeffrey L Wilson, David L R01 CA084433/CA/NCI NIH HHS/United States R01-CA84433-01/CA/NCI NIH HHS/United States R33-CA88144-01/CA/NCI NIH HHS/United States Research Support, U.S. Gov't, P.H.S. England Physics in medicine and biology Phys Med Biol. 2002 Mar 7;47(5):823-38. PY - 2002 SN - 0031-9155 (Print) 0031-9155 (Linking) SP - 823-38 ST - Automatic MR volume registration and its evaluation for the pelvis and prostate T2 - Phys Med Biol TI - Automatic MR volume registration and its evaluation for the pelvis and prostate VL - 47 ID - 95 ER - TY - JOUR AB - A three-dimensional warping registration algorithm was created and compared to rigid body registration of magnetic resonance (MR) pelvic volumes including the prostate. The rigid body registration method combines the advantages of mutual information (MI) and correlation coefficient at different resolutions. Warping registration is based upon independent optimization of many interactively placed control points (CP's) using MI and a thin plate spline transformation. More than 100 registration experiments with 17 MR volume pairs determined the quality of registration under conditions simulating potential interventional MRI-guided treatments of prostate cancer. For image pairs that stress rigid body registration (e.g. supine, the diagnostic position, and legs raised, the treatment position), both visual and numerical evaluation methods showed that warping consistently worked better than rigid body. Experiments showed that approximately 180 strategically placed CP's were sufficiently expressive to capture important features of the deformation. AD - Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA. AN - 12631511 AU - Fei, B. AU - Kemper, C. AU - Wilson, D. L. DA - Jul-Aug DO - S0895611102000939 [pii] DP - Nlm ET - 2003/03/13 IS - 4 KW - Algorithms Humans Imaging, Three-Dimensional Magnetic Resonance Imaging/ methods Male Models, Statistical Pelvis/ pathology Posture Prostate/ pathology Prostatic Neoplasms/ therapy Radiometry/ methods LA - eng N1 - Fei, Baowei Kemper, Corey Wilson, David L R01 CA084433/CA/NCI NIH HHS/United States R01-CA84433-01/CA/NCI NIH HHS/United States R33-CA88144-01/CA/NCI NIH HHS/United States Comparative Study Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. United States Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Comput Med Imaging Graph. 2003 Jul-Aug;27(4):267-81. PY - 2003 SN - 0895-6111 (Print) 0895-6111 (Linking) SP - 267-81 ST - A comparative study of warping and rigid body registration for the prostate and pelvic MR volumes T2 - Comput Med Imaging Graph TI - A comparative study of warping and rigid body registration for the prostate and pelvic MR volumes VL - 27 ID - 92 ER - TY - JOUR AB - In this study, we registered live-time interventional magnetic resonance imaging (iMRI) slices with a previously obtained high-resolution MRI volume that in turn can be registered with a variety of functional images, e.g., PET, SPECT, for tumor targeting. We created and evaluated a slice-to-volume (SV) registration algorithm with special features for its potential use in iMRI-guided radio-frequency (RF) thermal ablation of prostate cancer. The algorithm features included a multiresolution approach, two similarity measures, and automatic restarting to avoid local minima. Imaging experiments were performed on volunteers using a conventional 1.5-T MR scanner and a clinical 0.2-T C-arm iMRI system under realistic conditions. Both high-resolution MR volumes and actual iMRI image slices were acquired from the same volunteers. Actual and simulated iMRI images were used to test the dependence of SV registration on image noise, receive coil inhomogeneity, and RF needle artifacts. To quantitatively assess registration, we calculated the mean voxel displacement over a volume of interest between SV registration and volume-to-volume registration, which was previously shown to be quite accurate. More than 800 registration experiments were performed. For transverse image slices covering the prostate, the SV registration algorithm was 100% successful with an error of <2 mm, and the average and standard deviation was only 0.4 mm +/- 0.2 mm. Visualizations such as combined sector display and contour overlay showed excellent registration of the prostate and other organs throughout the pelvis. Error was greater when an image slice was obtained at other orientations and positions, mostly because of inconsistent image content such as that from variable rectal And bladder filling. These preliminary experiments indicate that MR SV registration is sufficiently accurate to aid image-guided therapy. AN - WOS:000183078600005 AU - Fei, B. W. AU - Duerk, J. L. AU - Boll, D. T. AU - Lewin, J. S. AU - Wilson, D. L. DA - Apr DO - 10.1109/tmi.2003.809078 IS - 4 N1 - Times Cited: 54 Lewin, Jonathan/A-4331-2009; Fei, Baowei /E-6898-2014 Fei, Baowei /0000-0002-9123-9484 0 54 PY - 2003 SN - 0278-0062 SP - 515-525 ST - Slice-to-volume registration and its potential application to interventional MRI-guided radio-frequency thermal ablation of prostate cancer T2 - Ieee Transactions on Medical Imaging TI - Slice-to-volume registration and its potential application to interventional MRI-guided radio-frequency thermal ablation of prostate cancer UR - ://WOS:000183078600005 VL - 22 ID - 293 ER - TY - JOUR AB - We are investigating interventional MRI (iMRI) guided radiofrequency thermal ablation for the minimally invasive treatment of the prostate cancer. Nuclear medicine can detect and localize tumor in the prostate not reliably seen in MRI. We intend to combine the advantages of functional images such as nuclear medicine SPECT with iMRI-guided treatments. Our concept is to first register the low-resolution SPECT with a high-resolution MRI volume. Then by registering the high-resolution MR image with live-time iMRI acquisitions, we can, in turn, map the functional data and high-resolution anatomic information to live-time iMRI images for improved tumor targeting. For the first step, we used a three-dimensional mutual information registration method. For the latter, we developed a robust slice to volume (SV) registration algorithm with special features. The concept was tested using image data from three patients and three volunteers. The SV registration accuracy was 0.4 mm +/- 0.2 mm as compared to our volume-to-volume registration that was previously shown to be quite accurate for these image pairs. With our image registration and fusion software, simulation experiments show that it is quite feasible to incorporate SPECT and high-resolution MRI into the iMRI-guided minimally invasive treatment procedures. AN - WOS:000220883100029 AU - Fei, B. W. AU - Lee, Z. H. AU - Boll, D. T. AU - Duerk, J. L. AU - Sodee, D. B. AU - Lewin, J. S. AU - Wilson, D. L. DA - Feb DO - 10.1109/tns.2003.823027 IS - 1 N1 - Times Cited: 23 1 Lewin, Jonathan/A-4331-2009; Fei, Baowei /E-6898-2014 Fei, Baowei /0000-0002-9123-9484 0 23 PY - 2004 SN - 0018-9499 SP - 177-183 ST - Registration and fusion of SPECT, high-resolution MRI, and interventional MRI for thermal ablation of prostate cancer T2 - Ieee Transactions on Nuclear Science TI - Registration and fusion of SPECT, high-resolution MRI, and interventional MRI for thermal ablation of prostate cancer UR - ://WOS:000220883100029 VL - 51 ID - 297 ER - TY - JOUR AB - RATIONALE AND OBJECTIVES: Three-dimensional (3D) nonrigid image registration for potential applications in prostate cancer treatment and interventional magnetic resonance (iMRI) imaging-guided therapies were investigated. MATERIALS AND METHODS: An almost fully automated 3D nonrigid registration algorithm using mutual information and a thin plate spline (TPS) transformation for MR images of the prostate and pelvis were created and evaluated. In the first step, an automatic rigid body registration with special features was used to capture the global transformation. In the second step, local feature points (FPs) were registered using mutual information. An operator entered only five FPs located at the prostate center, left and right hip joints, and left and right distal femurs. The program automatically determined and optimized other FPs at the external pelvic skin surface and along the femurs. More than 600 control points were used to establish a TPS transformation for deformation of the pelvic region and prostate. Ten volume pairs were acquired from three volunteers in the diagnostic (supine) and treatment positions (supine with legs raised). RESULTS: Various visualization techniques showed that warping rectified the significant pelvic misalignment by the rigid-body method. Gray-value measures of registration quality, including mutual information, correlation coefficient, and intensity difference, all improved with warping. The distance between prostate 3D centroids was 0.7 +/- 0.2 mm after warping compared with 4.9 +/- 3.4 mm with rigid-body registration. CONCLUSION: Semiautomatic nonrigid registration works better than rigid-body registration when patient position is changed greatly between acquisitions. It could be a useful tool for many applications in the management of prostate. AD - Department of Radiology, Case Western Reserve University and University Hospitals of Cleveland, 11100 Euclid Avenue, Cleveland, OH 44106, USA. baowei.fei@case.edu AN - 16039535 AU - Fei, B. AU - Duerk, J. L. AU - Sodee, D. B. AU - Wilson, D. L. DA - Jul DO - S1076-6332(05)00277-1 [pii] 10.1016/j.acra.2005.03.063 [doi] DP - Nlm ET - 2005/07/26 IS - 7 KW - Algorithms Femur/pathology Humans Imaging, Three-Dimensional Magnetic Resonance Imaging/ methods Male Pelvis/ pathology Prostatic Neoplasms/ pathology/therapy LA - eng N1 - Fei, Baowei Duerk, Jeffrey L Sodee, D Bruce Wilson, David L R01 CA084433/CA/NCI NIH HHS/United States R33-CA88144/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. United States Academic radiology Acad Radiol. 2005 Jul;12(7):815-24. PY - 2005 SN - 1076-6332 (Print) 1076-6332 (Linking) SP - 815-24 ST - Semiautomatic nonrigid registration for the prostate and pelvic MR volumes T2 - Acad Radiol TI - Semiautomatic nonrigid registration for the prostate and pelvic MR volumes VL - 12 ID - 89 ER - TY - JOUR AB - This chapter describes automatic three-dimensional registration techniques for magnetic resonance images of carotid vessels. The immediate applications include atherosclerotic plaque characterization and plaque burden quantification vector-based segmentation using dark blood MR images having multiple contrast weightings (proton density (PD), T1, and T2). Another application is the measurement of disease progression and regression with drug trials. A normalized mutual information registration algorithm is applied to compensate movements between image acquisitions. PD, T1, and T2 images were acquired from patients and volunteers and then matched for image analysis. Visualization methods such as contour overlap showed that vessels well aligned after registration. Distance measurements from the landmarks indicated that the registration method worked well with an error of less than 1-mm. AD - Case Western Reserve University, Cleveland, OH, USA. AN - 15923750 AU - Fei, B. AU - Suri, J. S. AU - Wilson, D. L. DP - Nlm ET - 2005/06/01 KW - Algorithms Humans Image Interpretation, Computer-Assisted Magnetic Resonance Imaging LA - eng N1 - Fei, Baowei Suri, Jasjit S Wilson, David L R01 CA084433/CA/NCI NIH HHS/United States Netherlands Studies in health technology and informatics Stud Health Technol Inform. 2005;113:394-411. PY - 2005 SN - 0926-9630 (Print) 0926-9630 (Linking) SP - 394-411 ST - Three-Dimensional Volume Registration of Carotid MR Images T2 - Stud Health Technol Inform TI - Three-Dimensional Volume Registration of Carotid MR Images VL - 113 ID - 90 ER - TY - JOUR AB - We are investigating imaging techniques to study the tumor response to photodynamic therapy (PDT). Positron emission tomography (PET) can provide physiological and functional information. High-resolution magnetic resonance imaging (MRI) can provide anatomical and morphological changes. Image registration can combine MRI and PET images for improved tumor monitoring. In this study, we acquired high-resolution MRI and microPET F-18-fluorodeoxyglucose (FDG) images from C3H mice with RIF-1 tumors that were treated with Pc 4-based PDT. We developed two registration methods for this application. For registration of the whole mouse body, we used an automatic three-dimensional, normalized mutual information algorithm. For tumor registration, we developed a finite element model (FEM)-based deformable registration scheme. To assess the quality of whole body registration, we performed slice-by-slice review of both image volumes; manually segmented feature organs, such as the left and right kidneys and the bladder, in each slice; and computed the distance between corresponding centroids. Over 40 volume registration experiments were performed with MRI and microPET images. The distance between corresponding centroids of organs was 1.5 +/- 0.4 mm, which is about 2 pixels of microPET images. The mean volume overlap ratios for tumors were 94.7% and 86.3% for the deformable and rigid registration methods, respectively. Registration of high-resolution MRI and microPET. images combines anatomical and functional information of the tumors and provides a useful tool for evaluating photodynamic therapy. (c) 2006 American Association of Physicists in Medicine. AN - WOS:000236263900018 AU - Fei, B. W. AU - Wang, H. S. AU - Muzic, R. F. AU - Flask, C. AU - Wilson, D. L. AU - Duerk, J. L. AU - Feyes, D. K. AU - Oleinick, N. L. DA - Mar DO - 10.1118/1.2163831 IS - 3 N1 - Times Cited: 27 wang, hesheng/A-6260-2013; Fei, Baowei /E-6898-2014 Fei, Baowei /0000-0002-9123-9484 0 27 PY - 2006 SN - 0094-2405 SP - 753-760 ST - Deformable and rigid registration of MRI and microPET images for photodynamic therapy of cancer in mice T2 - Medical Physics TI - Deformable and rigid registration of MRI and microPET images for photodynamic therapy of cancer in mice UR - ://WOS:000236263900018 VL - 33 ID - 299 ER - TY - JOUR AU - Chen, Xiang AU - Gilkeson, Robert C AU - Fei, Baowei IS - 12 L1 - internal-pdf://0978233545/Chen-2007-Automatic 3D‐to‐2D registration for.pdf PY - 2007 SN - 2473-4209 SP - 4934-4943 ST - Automatic 3D‐to‐2D registration for CT and dual‐energy digital radiography for calcification detection T2 - Medical physics TI - Automatic 3D‐to‐2D registration for CT and dual‐energy digital radiography for calcification detection UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2743028/pdf/nihms113518.pdf VL - 34 ID - 114 ER - TY - JOUR AU - Fei, Baowei AU - Wang, Hesheng AU - Meyers, Joseph D AU - Feyes, Denise K AU - Oleinick, Nancy L AU - Duerk, Jeffrey L IS - 9 L1 - internal-pdf://2833302472/Fei-2007-High‐field magnetic resonance imaging.pdf PY - 2007 SN - 1096-9101 SP - 723-730 ST - High‐field magnetic resonance imaging of the response of human prostate cancer to Pc 4‐based photodynamic therapy in an animal model T2 - Lasers in surgery and medicine TI - High‐field magnetic resonance imaging of the response of human prostate cancer to Pc 4‐based photodynamic therapy in an animal model UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2719260/pdf/nihms-113521.pdf VL - 39 ID - 107 ER - TY - JOUR AU - Haaga, JR AU - Exner, A AU - Fei, B AU - Seftel, AD IS - 1 L1 - internal-pdf://2885477942/Haaga-2007-Semiquantitative imaging measuremen.pdf PY - 2007 SP - 110 ST - Semiquantitative imaging measurement of baseline and vasomodulated normal prostatic blood flow using sildenafil T2 - International journal of impotence research TI - Semiquantitative imaging measurement of baseline and vasomodulated normal prostatic blood flow using sildenafil UR - http://www.nature.com/ijir/journal/v19/n1/pdf/3901486a.pdf VL - 19 ID - 139 ER - TY - JOUR AB - Recent technological improvements have led to increasing clinical use of interface pressure mapping for seating pressure evaluation, which often requires repeated assessments. However, clinical conditions cannot be controlled as closely as research settings, thereby creating challenges to statistical analysis of data. A multistage longitudinal analysis and self-registration (LASR) technique is introduced that emphasizes real-time interface pressure image analysis in three dimensions. Suitable for use in clinical settings, LASR is composed of several modern statistical components, including a segmentation method. The robustness of our segmentation method is also shown. Application of LASR to analysis of data from neuromuscular electrical stimulation (NMES) experiments confirms that NMES improves static seating pressure distributions in the sacral-ischial region over time. Dynamic NMES also improves weight-shifting over time. These changes may reduce the risk of pressure ulcer development. AD - Cleveland Advanced Platform Technology Center, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA. kmb3@case.edu AN - 18712638 AU - Bogie, K. AU - Wang, X. AU - Fei, B. AU - Sun, J. C2 - 2729147 DP - Nlm ET - 2008/08/21 IS - 4 KW - Algorithms Buttocks/blood supply Electric Stimulation Electric Stimulation Therapy Humans Imaging, Three-Dimensional Longitudinal Studies Pressure Pressure Ulcer/ prevention & control Risk Factors Time Factors Wheelchairs L1 - internal-pdf://3019139496/Bogie-2008-New technique for real-time interfa.pdf LA - eng N1 - Bogie, Kath Wang, Xiaofeng Fei, Baowei Sun, Jiayang R21 CA120536/CA/NCI NIH HHS/United States R21 CA120536-01/CA/NCI NIH HHS/United States R21 CA120536-02/CA/NCI NIH HHS/United States R21CA120536/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S. United States Journal of rehabilitation research and development Nihms113522 J Rehabil Res Dev. 2008;45(4):523-35, 10 p following 535. PY - 2008 SN - 1938-1352 (Electronic) 0748-7711 (Linking) SP - 523-35, 10 p following 535 ST - New technique for real-time interface pressure analysis: getting more out of large image data sets T2 - J Rehabil Res Dev TI - New technique for real-time interface pressure analysis: getting more out of large image data sets VL - 45 ID - 75 ER - TY - JOUR AB - A highly efficient drug vector for photodynamic therapy (PDT) drug delivery was developed by synthesizing PEGylated gold nanoparticle conjugates, which act as a water-soluble and biocompatible "cage" that allows delivery of a hydrophobic drug to its site of PDT action. The dynamics of drug release in vitro in a two-phase solution system and in vivo in cancer-bearing mice indicates that the process of drug delivery is highly efficient, and passive targeting prefers the tumor site. With the Au NP-Pc 4 conjugates, the drug delivery time required for PDT has been greatly reduced to less than 2 h, compared to 2 days for the free drug. AD - Center for Chemical Dynamics and Nanomaterials Research, Department of Chemistry, Case Western Reserve University, Cleveland, Ohio 44106, USA. AN - 18642918 AU - Cheng, Y. AU - A, C. Samia AU - Meyers, J. D. AU - Panagopoulos, I. AU - Fei, B. AU - Burda, C. C2 - 2719258 DA - Aug 13 DO - 10.1021/ja801631c [doi] DP - Nlm ET - 2008/07/23 IS - 32 KW - Animals Drug Delivery Systems Gold/ chemistry Indoles/ administration & dosage/chemistry Metal Nanoparticles/ chemistry Mice Mice, Nude Neoplasms/ drug therapy Photochemotherapy Polyethylene Glycols/chemistry Radiation-Sensitizing Agents/ administration & dosage/chemistry Singlet Oxygen/analysis Spectrometry, Fluorescence Spectrophotometry, Ultraviolet L1 - internal-pdf://1999828385/Cheng-2008-Highly efficient drug delivery with.pdf LA - eng N1 - Cheng, Yu C Samia, Anna Meyers, Joseph D Panagopoulos, Irene Fei, Baowei Burda, Clemens R21 CA120536/CA/NCI NIH HHS/United States R21 CA120536-01/CA/NCI NIH HHS/United States R21 CA120536-02/CA/NCI NIH HHS/United States CA120536/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. United States Journal of the American Chemical Society Nihms113517 J Am Chem Soc. 2008 Aug 13;130(32):10643-7. doi: 10.1021/ja801631c. Epub 2008 Jul 22. PY - 2008 SN - 1520-5126 (Electronic) 0002-7863 (Linking) SP - 10643-7 ST - Highly efficient drug delivery with gold nanoparticle vectors for in vivo photodynamic therapy of cancer T2 - J Am Chem Soc TI - Highly efficient drug delivery with gold nanoparticle vectors for in vivo photodynamic therapy of cancer UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2719258/pdf/nihms-113517.pdf VL - 130 ID - 77 ER - TY - JOUR AB - A fully automatic. multiscale fuzzy C-means (MsFCM) classification method for MR images is presented in this paper. We use a diffusion filter to process MR images and to construct a multiscale image series. A multiscale fuzzy C-means classification method is applied along the scales from the coarse to fine levels. The objective function of the conventional fuzzy C-means (FCM) method is modified to allow multiscale classification processing where the result from a coarse scale supervises the classification in the next fine scale. The method is robust for noise and low-contrast MR images because of its multiscale diffusion filtering scheme. The new method was compared with the conventional FCM method and a modified FCM (MFCM) method. Validation studies were performed on synthesized images with various contrasts and on the McGill brain MR image database. Our MsFCM method consistently performed better than the conventional FCM and MFCM methods. The MsFCM method achieved an overlap ratio of greater than 90% as validated by the ground truth. Experiments results on real MR images were given to demonstrate the effectiveness of the proposed method. Our multiscale fuzzy C-means classification method is accurate and robust for various MR images. It can provide a quantitative tool for neuroimaging and other applications. (C) 2008 Elsevier B.V. All rights reserved. AN - WOS:000265224200001 AU - Wang, Hesheng AU - Fei, Baowei DA - Apr DO - 10.1016/j.media.2008.06.014 IS - 2 N1 - Times Cited: 39 wang, hesheng/A-6260-2013; Fei, Baowei /E-6898-2014 Fei, Baowei /0000-0002-9123-9484 1 48 PY - 2009 SN - 1361-8415 SP - 193-202 ST - A modified fuzzy C-means classification method using a multiscale diffusion filtering scheme T2 - Medical Image Analysis TI - A modified fuzzy C-means classification method using a multiscale diffusion filtering scheme UR - ://WOS:000265224200001 VL - 13 ID - 306 ER - TY - JOUR AU - Fei, Baowei AU - Wang, Hesheng AU - Wu, Chunying AU - Chiu, Song-mao IS - 1 L1 - internal-pdf://0783974358/Fei-2010-Choline PET for monitoring early tumo.pdf PY - 2010 SN - 0161-5505 SP - 130-138 ST - Choline PET for monitoring early tumor response to photodynamic therapy T2 - Journal of Nuclear Medicine TI - Choline PET for monitoring early tumor response to photodynamic therapy UR - http://jnm.snmjournals.org/content/51/1/130.full.pdf VL - 51 ID - 111 ER - TY - JOUR AB - PURPOSE: To examine diffusion-weighted MRI (DW-MRI) for assessing the early tumor response to photodynamic therapy (PDT). MATERIALS AND METHODS: Subcutaneous tumor xenografts of human prostate cancer cells (CWR22) were initiated in athymic nude mice. A second-generation photosensitizer, Pc 4, was delivered to each animal by a tail vein injection 48 h before laser illumination. A dedicated high-field (9.4 Tesla) small animal MR scanner was used to acquire diffusion-weighted MR images pre-PDT and 24 h after the treatment. DW-MRI and apparent diffusion coefficients (ADC) were analyzed for 24 treated and 5 control mice with photosensitizer only or laser light only. Tumor size, prostate specific antigen (PSA) level, and tumor histology were obtained at different time points to examine the treatment effect. RESULTS: Treated mice showed significant tumor size shrinkage and decrease of PSA level within 7 days after the treatment. The average ADC of the 24 treated tumors increased 24 h after PDT (P < 0.001) comparing with pre-PDT. The average ADC was 0.511 +/- 0.119 x 10(-3) mm(2)/s pre-PDT and 0.754 +/- 0.181 x 10(-3) mm(2)/s 24 h after the PDT. There is no significant difference in ADC values pre-PDT and 24 h after PDT in the control tumors (P = 0.20). CONCLUSION: The change of tumor ADC values measured by DW-MRI may provide a noninvasive imaging marker for monitoring tumor response to Pc 4-PDT as early as 24 h. AD - Emory Center for Systems Imaging, Department of Radiology, Emory University, Atlanta, Georgia 30329, USA. AN - 20677270 AU - Wang, H. AU - Fei, B. C2 - 3076282 DA - Aug DO - 10.1002/jmri.22247 [doi] DP - Nlm ET - 2010/08/03 IS - 2 KW - Animals Biomarkers, Tumor Cell Line, Tumor Diffusion Diffusion Magnetic Resonance Imaging/ methods Humans Male Mice Mice, Nude Neoplasm Transplantation Photochemotherapy/ methods Photosensitizing Agents/pharmacology Prostatic Neoplasms/ pathology/ therapy Treatment Outcome L1 - internal-pdf://0470394801/Wang-2010-Diffusion-weighted MRI for monitorin.pdf LA - eng N1 - Wang, Hesheng Fei, Baowei R21 CA120536-02/CA/NCI NIH HHS/United States R21 CA120536/CA/NCI NIH HHS/United States R24CA110943/CA/NCI NIH HHS/United States UL1 TR000454/TR/NCATS NIH HHS/United States R21 CA120536-01/CA/NCI NIH HHS/United States R21CA120536/CA/NCI NIH HHS/United States R24 CA110943/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't United States Journal of magnetic resonance imaging : JMRI Nihms255624 J Magn Reson Imaging. 2010 Aug;32(2):409-17. doi: 10.1002/jmri.22247. PY - 2010 SN - 1522-2586 (Electronic) 1053-1807 (Linking) SP - 409-17 ST - Diffusion-weighted MRI for monitoring tumor response to photodynamic therapy T2 - J Magn Reson Imaging TI - Diffusion-weighted MRI for monitoring tumor response to photodynamic therapy UR - http://onlinelibrary.wiley.com/store/10.1002/jmri.22247/asset/22247_ftp.pdf?v=1&t=j721p69c&s=0557d2a8a0cf81d29172ea58ff91472ae1d60d4f VL - 32 ID - 68 ER - TY - JOUR AB - PURPOSE: Classification of magnetic resonance (MR) images has many clinical and research applications. Because of multiple factors such as noise, intensity inhomogeneity, and partial volume effects, MR image classification can be challenging. Noise in MRI can cause the classified regions to become disconnected. Partial volume effects make the assignment of a single class to one region difficult. Because of intensity inhomogeneity, the intensity of the same tissue can vary with respect to the location of the tissue within the same image. The conventional "hard" classification method restricts each pixel exclusively to one class and often results in crisp results. Fuzzy C-mean (FCM) classification or "soft" segmentation has been extensively applied to MR images, in which pixels are partially classified into multiple classes using varying memberships to the classes. Standard FCM, however, is sensitive to noise and cannot effectively compensate for intensity inhomogeneities. This paper presents a method to obtain accurate MR brain classification using a modified multiscale and multiblock FCM. METHODS: An automatic, multiscale and multiblock fuzzy C-means (MsbFCM) classification method with MR intensity correction is presented in this paper. We use a bilateral filter to process MR images and to build a multiscale image series by increasing the standard deviation of spatial function and by reducing the standard deviation of range function. At each scale, we separate the image into multiple blocks and for every block a multiscale fuzzy C-means classification method is applied along the scales from the coarse to fine levels in order to overcome the effect of intensity inhomogeneity. The result from a coarse scale supervises the classification in the next fine scale. The classification method is tested with noisy MR images with intensity inhomogeneity. RESULTS: Our method was compared with the conventional FCM, a modified FCM (MFCM) and multiscale FCM (MsFCM) method. Validation studies were performed on synthesized images with various contrasts, on the simulated brain MR database, and on real MR images. Our MsbFCM method consistently performed better than the conventional FCM, MFCM, and MsFCM methods. The MsbFCM method achieved an overlap ratio of 91% or higher. Experimental results using real MR images demonstrate the effectiveness of the proposed method. Our MsbFCM classification method is accurate and robust for various MR images. CONCLUSIONS: As our classification method did not assume a Gaussian distribution of tissue intensity, it could be used on other image data for tissue classification and quantification. The automatic classification method can provide a useful quantification tool in neuroimaging and other applications. AD - Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia 30329, USA. AN - 21815363 AU - Yang, X. AU - Fei, B. C2 - 3117893 DA - Jun DO - 10.1118/1.3584199 [doi] DP - Nlm ET - 2011/08/06 IS - 6 KW - Algorithms Brain Humans Image Processing, Computer-Assisted/ methods Magnetic Resonance Imaging/ methods L1 - internal-pdf://0602015128/Yang-2011-A multiscale and multiblock fuzzy C-.pdf LA - eng N1 - Yang, Xiaofeng Fei, Baowei R01CA156775/CA/NCI NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States UL1RR025008/RR/NCRR NIH HHS/United States UL1 TR000454/TR/NCATS NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States UL1 RR025008/RR/NCRR NIH HHS/United States P50 CA128301/CA/NCI NIH HHS/United States P50 CA128613/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't United States Medical physics Med Phys. 2011 Jun;38(6):2879-91. PY - 2011 SN - 0094-2405 (Print) 0094-2405 (Linking) SP - 2879-91 ST - A multiscale and multiblock fuzzy C-means classification method for brain MR images T2 - Med Phys TI - A multiscale and multiblock fuzzy C-means classification method for brain MR images UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117893/pdf/MPHYA6-000038-002879_1.pdf VL - 38 ID - 59 ER - TY - JOUR AB - Based on the Radon transform, a wavelet multiscale denoising method is proposed for MR images. The approach explicitly accounts for the Rician nature of MR data. Based on noise statistics we apply the Radon transform to the original MR images and use the Gaussian noise model to process the MR sinogram image. A translation invariant wavelet transform is employed to decompose the MR 'sinogram' into multiscales in order to effectively denoise the images. Based on the nature of Rician noise we estimate noise variance in different scales. For the final denoised sinogram we apply the inverse Radon transform in order to reconstruct the original MR images. Phantom, simulation brain MR images, and human brain MR images were used to validate our method. The experiment results show the superiority of the proposed scheme over the traditional methods. Our method can reduce Rician noise while preserving the key image details and features. The wavelet denoising method can have wide applications in MRI as well as other imaging modalities. AD - Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China ; Department of Radiology, Emory University, Atlanta, GA 30329, USA. AN - 23853425 AU - Yang, X. AU - Fei, B. C2 - 3707516 DA - Feb 01 DO - 10.1088/0957-0233/22/2/025803 [doi] DP - Nlm ET - 2011/02/01 IS - 2 L1 - internal-pdf://1723498345/Yang-2011-A wavelet multiscale denoising algor.pdf LA - eng N1 - Yang, Xiaofeng Fei, Baowei R01 CA156775/CA/NCI NIH HHS/United States R01 CA156775-01/CA/NCI NIH HHS/United States R01 CA156775-02/CA/NCI NIH HHS/United States R01 CA156775-03/CA/NCI NIH HHS/United States England Measurement science & technology Nihms362727 Meas Sci Technol. 2011 Feb 1;22(2):25803. PY - 2011 SN - 0957-0233 (Print) 0957-0233 (Linking) SP - 25803 ST - A wavelet multiscale denoising algorithm for magnetic resonance (MR) images T2 - Meas Sci Technol TI - A wavelet multiscale denoising algorithm for magnetic resonance (MR) images UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3707516/pdf/nihms362727.pdf VL - 22 ID - 63 ER - TY - JOUR AB - PURPOSE: Transrectal ultrasound (TRUS) imaging is clinically used in prostate biopsy and therapy. Segmentation of the prostate on TRUS images has many applications. In this study, a three-dimensional (3D) segmentation method for TRUS images of the prostate is presented for 3D ultrasound-guided biopsy. METHODS: This segmentation method utilizes a statistical shape, texture information, and intensity profiles. A set of wavelet support vector machines (W-SVMs) is applied to the images at various subregions of the prostate. The W-SVMs are trained to adaptively capture the features of the ultrasound images in order to differentiate the prostate and nonprostate tissue. This method consists of a set of wavelet transforms for extraction of prostate texture features and a kernel-based support vector machine to classify the textures. The voxels around the surface of the prostate are labeled in sagittal, coronal, and transverse planes. The weight functions are defined for each labeled voxel on each plane and on the model at each region. In the 3D segmentation procedure, the intensity profiles around the boundary between the tentatively labeled prostate and nonprostate tissue are compared to the prostate model. Consequently, the surfaces are modified based on the model intensity profiles. The segmented prostate is updated and compared to the shape model. These two steps are repeated until they converge. Manual segmentation of the prostate serves as the gold standard and a variety of methods are used to evaluate the performance of the segmentation method. RESULTS: The results from 40 TRUS image volumes of 20 patients show that the Dice overlap ratio is 90.3% +/- 2.3% and that the sensitivity is 87.7% +/- 4.9%. CONCLUSIONS: The proposed method provides a useful tool in our 3D ultrasound image-guided prostate biopsy and can also be applied to other applications in the prostate. AD - Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA 30329, USA. AN - 22755682 AU - Akbari, H. AU - Fei, B. C2 - 3360689 DA - Jun DO - 10.1118/1.4709607 [doi] DP - Nlm ET - 2012/07/05 IS - 6 KW - Humans Imaging, Three-Dimensional/ methods Male Prostate/diagnostic imaging Support Vector Machine Ultrasonography/ methods L1 - internal-pdf://3681669783/Akbari-2012-3D ultrasound image segmentation u.pdf LA - eng N1 - Akbari, Hamed Fei, Baowei R01CA156775/CA/NCI NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States P50CA128613/CA/NCI NIH HHS/United States UL1 TR000454/TR/NCATS NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States UL1 RR025008/RR/NCRR NIH HHS/United States P50 CA128301/CA/NCI NIH HHS/United States P50 CA128613/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. United States Medical physics Med Phys. 2012 Jun;39(6):2972-84. doi: 10.1118/1.4709607. PY - 2012 SN - 0094-2405 (Print) 0094-2405 (Linking) SP - 2972-84 ST - 3D ultrasound image segmentation using wavelet support vector machines T2 - Med Phys TI - 3D ultrasound image segmentation using wavelet support vector machines UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3360689/pdf/MPHYA6-000039-002972_1.pdf VL - 39 ID - 54 ER - TY - JOUR AB - Hyperspectral imaging (HSI) is an emerging modality for various medical applications. Its spectroscopic data might be able to be used to noninvasively detect cancer. Quantitative analysis is often necessary in order to differentiate healthy from diseased tissue. We propose the use of an advanced image processing and classification method in order to analyze hyperspectral image data for prostate cancer detection. The spectral signatures were extracted and evaluated in both cancerous and normal tissue. Least squares support vector machines were developed and evaluated for classifying hyperspectral data in order to enhance the detection of cancer tissue. This method was used to detect prostate cancer in tumor-bearing mice and on pathology slides. Spatially resolved images were created to highlight the differences of the reflectance properties of cancer versus those of normal tissue. Preliminary results with 11 mice showed that the sensitivity and specificity of the hyperspectral image classification method are 92.8% to 2.0% and 96.9% to 1.3%, respectively. Therefore, this imaging method may be able to help physicians to dissect malignant regions with a safe margin and to evaluate the tumor bed after resection. This pilot study may lead to advances in the optical diagnosis of prostate cancer using HSI technology. AD - Emory University, Department of Radiology and Imaging Sciences, Atlanta, Georgia 30329, USA. AN - 22894488 AU - Akbari, H. AU - Halig, L. V. AU - Schuster, D. M. AU - Osunkoya, A. AU - Master, V. AU - Nieh, P. T. AU - Chen, G. Z. AU - Fei, B. C2 - 3608529 DA - Jul DO - 10.1117/1.JBO.17.7.076005 [doi] DP - Nlm ET - 2012/08/17 IS - 7 KW - Algorithms Animals Artificial Intelligence Cell Line, Tumor Image Enhancement/methods Image Interpretation, Computer-Assisted/ methods Male Mice Mice, Nude Optical Imaging/ methods Pattern Recognition, Automated/ methods Prostatic Neoplasms/ pathology Reproducibility of Results Sensitivity and Specificity Spectrum Analysis/ methods L1 - internal-pdf://4245577641/Akbari-2012-Hyperspectral imaging and quantita.pdf LA - eng N1 - Akbari, Hamed Halig, Luma V Schuster, David M Osunkoya, Adeboye Master, Viraj Nieh, Peter T Chen, Georgia Z Fei, Baowei R01CA156775/CA/NCI NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States P50CA128613/CA/NCI NIH HHS/United States UL1 TR000454/TR/NCATS NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States UL1 RR025008/RR/NCRR NIH HHS/United States P50 CA128301/CA/NCI NIH HHS/United States P50 CA128613/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural United States Journal of biomedical optics J Biomed Opt. 2012 Jul;17(7):076005. doi: 10.1117/1.JBO.17.7.076005. PY - 2012 SN - 1560-2281 (Electronic) 1083-3668 (Linking) SP - 076005 ST - Hyperspectral imaging and quantitative analysis for prostate cancer detection T2 - J Biomed Opt TI - Hyperspectral imaging and quantitative analysis for prostate cancer detection UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3608529/pdf/JBO-017-076005.pdf VL - 17 ID - 52 ER - TY - JOUR AB - PURPOSE: Combined MRPET is a relatively new, hybrid imaging modality. A human MRPET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MRPET for brain imaging. METHODS: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MRPET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [(11)C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. RESULTS: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 +/- 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. CONCLUSIONS: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MRPET. AD - Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA. bfei@emory.edu AN - 23039679 AU - Fei, B. AU - Yang, X. AU - Nye, J. A. AU - Aarsvold, J. N. AU - Raghunath, N. AU - Cervo, M. AU - Stark, R. AU - Meltzer, C. C. AU - Votaw, J. R. C2 - 3477199 DA - Oct DO - 10.1118/1.4754796 [doi] DP - Nlm ET - 2012/10/09 IS - 10 KW - Algorithms Humans Image Processing, Computer-Assisted/ methods Magnetic Resonance Imaging/ methods Phantoms, Imaging Positron-Emission Tomography/ methods L1 - internal-pdf://3276788469/Fei-2012-MRPET quantification tools_ registrat.pdf LA - eng N1 - Fei, Baowei Yang, Xiaofeng Nye, Jonathon A Aarsvold, John N Raghunath, Nivedita Cervo, Morgan Stark, Rebecca Meltzer, Carolyn C Votaw, John R R01CA156775/CA/NCI NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States P50CA128613/CA/NCI NIH HHS/United States UL1RR025008/RR/NCRR NIH HHS/United States UL1 TR000454/TR/NCATS NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States UL1 RR025008/RR/NCRR NIH HHS/United States P50 CA128301/CA/NCI NIH HHS/United States P50 CA128613/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural United States Medical physics Med Phys. 2012 Oct;39(10):6443-54. doi: 10.1118/1.4754796. PY - 2012 SN - 0094-2405 (Print) 0094-2405 (Linking) SP - 6443-54 ST - MRPET quantification tools: registration, segmentation, classification, and MR-based attenuation correction T2 - Med Phys TI - MRPET quantification tools: registration, segmentation, classification, and MR-based attenuation correction UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3477199/pdf/MPHYA6-000039-006443_1.pdf VL - 39 ID - 50 ER - TY - JOUR AB - Cardiovascular disease is the leading cause of global mortality, yet its early detection remains a vexing problem of modern medicine. Although the computed tomography (CT) calcium score predicts cardiovascular risk, relatively high cost ($250-400) and radiation dose (1-3 mSv) limit its universal utility as a screening tool. Dual-energy digital subtraction radiography (DE; <$60, 0.07 mSv) enables detection of calcified structures with high sensitivity. In this pilot study, we examined DE radiography's ability to quantify coronary artery calcification (CAC). We identified 25 patients who underwent non-contrast CT and DE chest imaging performed within 12 months using documented CAC as the major inclusion criteria. A DE calcium score was developed based on pixel intensity multiplied by the area of the calcified plaque. DE scores were plotted against CT scores. Subsequently, a validation cohort of 14 additional patients was independently evaluated to confirm the accuracy and precision of CAC quantification, yielding a total of 39 subjects. Among all subjects (n = 39), the DE score demonstrated a correlation coefficient of 0.87 (p < 0.0001) when compared with the CT score. For the 13 patients with CT scores of <400, the correlation coefficient was -0.26. For the 26 patients with CT scores of >/=400, the correlation coefficient yielded 0.86. This pilot study demonstrates the feasibility of DE radiography to identify patients at the highest cardiovascular risk. DE radiography's accuracy at lower scores remains unclear. Further evaluation of DE radiography as an inexpensive and low-radiation imaging tool to diagnose cardiovascular disease appears warranted. AD - Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA. AN - 21557030 AU - Mafi, J. N. AU - Fei, B. AU - Roble, S. AU - Dota, A. AU - Katrapati, P. AU - Bezerra, H. G. AU - Wang, H. AU - Wang, W. AU - Ciancibello, L. AU - Costa, M. AU - Simon, D. I. AU - Orringer, C. E. AU - Gilkeson, R. C. C2 - 3264713 DA - Feb DO - 10.1007/s10278-011-9385-y [doi] DP - Nlm ET - 2011/05/11 IS - 1 KW - Angiography, Digital Subtraction/ methods Calcinosis/ diagnostic imaging Case-Control Studies Coronary Angiography/methods Coronary Artery Disease/ diagnostic imaging/physiopathology Feasibility Studies Female Humans Male Middle Aged Pilot Projects Reproducibility of Results Retrospective Studies Sensitivity and Specificity Severity of Illness Index Statistics, Nonparametric L1 - internal-pdf://1709841335/Mafi-2012-Assessment of coronary artery calciu.pdf LA - eng N1 - Mafi, John N Fei, Baowei Roble, Sharon Dota, Anthony Katrapati, Prashanth Bezerra, Hiram G Wang, Hesheng Wang, Wei Ciancibello, Leslie Costa, Marco Simon, Daniel I Orringer, Carl E Gilkeson, Robert C R01 CA156775/CA/NCI NIH HHS/United States R21 CA120536/CA/NCI NIH HHS/United States R21CA120536/CA/NCI NIH HHS/United States Comparative Study Research Support, N.I.H., Extramural United States Journal of digital imaging J Digit Imaging. 2012 Feb;25(1):129-36. doi: 10.1007/s10278-011-9385-y. PY - 2012 SN - 1618-727X (Electronic) 0897-1889 (Linking) SP - 129-36 ST - Assessment of coronary artery calcium using dual-energy subtraction digital radiography T2 - J Digit Imaging TI - Assessment of coronary artery calcium using dual-energy subtraction digital radiography UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3264713/pdf/10278_2011_Article_9385.pdf VL - 25 ID - 60 ER - TY - JOUR AB - PURPOSE: To compare the estimate of normalized glandular dose in mammography and breast CT imaging obtained using the actual glandular tissue distribution in the breast to that obtained using the homogeneous tissue mixture approximation. METHODS: Twenty volumetric images of patient breasts were acquired with a dedicated breast CT prototype system and the voxels in the breast CT images were automatically classified into skin, adipose, and glandular tissue. The breasts in the classified images underwent simulated mechanical compression to mimic the conditions present during mammographic acquisition. The compressed thickness for each breast was set to that achieved during each patient's last screening cranio-caudal (CC) acquisition. The volumetric glandular density of each breast was computed using both the compressed and uncompressed classified images, and additional images were created in which all voxels representing adipose and glandular tissue were replaced by a homogeneous mixture of these two tissues in a proportion corresponding to each breast's volumetric glandular density. All four breast images (compressed and uncompressed; heterogeneous and homogeneous tissue) were input into Monte Carlo simulations to estimate the normalized glandular dose during mammography (compressed breasts) and dedicated breast CT (uncompressed breasts). For the mammography simulations the x-ray spectra used was that used during each patient's last screening CC acquisition. For the breast CT simulations, two x-ray spectra were used, corresponding to the x-ray spectra with the lowest and highest energies currently being used in dedicated breast CT prototype systems under clinical investigation. The resulting normalized glandular dose for the heterogeneous and homogeneous versions of each breast for each modality was compared. RESULTS: For mammography, the normalized glandular dose based on the homogeneous tissue approximation was, on average, 27% higher than that estimated using the true heterogeneous glandular tissue distribution (Wilcoxon Signed Rank Test p = 0.00046). For dedicated breast CT, the overestimation of normalized glandular dose was, on average, 8% (49 kVp spectrum, p = 0.00045) and 4% (80 kVp spectrum, p = 0.000089). Only two cases in mammography and two cases in dedicated breast CT with a tube voltage of 49 kVp resulted in lower dose estimates for the homogeneous tissue approximation compared to the heterogeneous tissue distribution. CONCLUSIONS: The normalized glandular dose based on the homogeneous tissue mixture approximation results in a significant overestimation of dose to the imaged breast. This overestimation impacts the use of dose estimates in absolute terms, such as for risk estimates, and may impact some comparative studies, such as when modalities or techniques with different x-ray energies are used. The error introduced by the homogeneous tissue mixture approximation in higher energy x-ray modalities, such as dedicated breast CT, although statistically significant, may not be of clinical concern. Further work is required to better characterize this overestimation and potentially develop new metrics or correction factors to better estimate the true glandular dose to breasts undergoing imaging with ionizing radiation. AD - Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University School of Medicine, 1701 Upper Gate Drive Northeast, Suite 5018, Atlanta, Georgia 30322, USA. isechop@emory.edu AN - 22894430 AU - Sechopoulos, I. AU - Bliznakova, K. AU - Qin, X. AU - Fei, B. AU - Feng, S. S. C2 - 3416880 DA - Aug DO - 10.1118/1.4737025 [doi] DP - Nlm ET - 2012/08/17 IS - 8 KW - Breast/pathology Breast Neoplasms/ diagnosis/ diagnostic imaging Computer Simulation Female Humans Mammography/methods Models, Statistical Monte Carlo Method Radiation, Ionizing Radiometry/ methods Reproducibility of Results Tissue Distribution Tomography, X-Ray Computed/ methods X-Rays L1 - internal-pdf://3691263608/Sechopoulos-2012-Characterization of the homog.pdf LA - eng N1 - Sechopoulos, Ioannis Bliznakova, Kristina Qin, Xulei Fei, Baowei Feng, Steve Si Jia R01CA156775/CA/NCI NIH HHS/United States R01CA163746/CA/NCI NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States P50 CA128301/CA/NCI NIH HHS/United States R01 CA163746/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't United States Medical physics Med Phys. 2012 Aug;39(8):5050-9. doi: 10.1118/1.4737025. PY - 2012 SN - 0094-2405 (Print) 0094-2405 (Linking) SP - 5050-9 ST - Characterization of the homogeneous tissue mixture approximation in breast imaging dosimetry T2 - Med Phys TI - Characterization of the homogeneous tissue mixture approximation in breast imaging dosimetry UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3416880/pdf/MPHYA6-000039-005050_1.pdf VL - 39 ID - 53 ER - TY - JOUR AB - PURPOSE: Partial volume effect in positron emission tomography (PET) can cause incorrect quantification of radiopharmaceutical uptake in functional imaging. A PET partial volume correction method is presented to attenuate partial volume blurring and to yield voxel-based corrected PET images. METHODS: By modeling partial volume effect as a convolution of point spread function of the PET scanner, the reconstructed PET images are corrected by iterative deconvolution with an edge-preserving smoothness constraint. The constraint is constructed to restore discontinuities extracted from coregistered MR images but maintains the smoothness in radioactivity distribution. The correction is implemented in a Bayesian deconvolution framework and is solved by a conjugate gradient method. The performance of the method was compared with the geometric transfer matrix (GTM) method on a simulated dataset. The method was evaluated on synthesized brain FDG-PET data and phantom MRI-PET experiments. RESULTS: The true PET activity of objects with a size of greater than the full-width at half maximum of the point spread function has been effectively restored in the simulated data. The partial volume correction method is quantitatively comparable to the GTM method. For synthesized FDG-PET with true activity 0 muci/cc for cerebrospinal fluid (CSF), 228 muci/cc for white matter (WM), and 621 muci/cc for gray matter (GM), the method has improved the radioactivity quantification from 186 +/- 16 muci/cc to 30 +/- 7 muci/cc in CSF, 317 +/- 15 muci/cc to 236 +/- 10 muci/cc for WM, 438 +/- 4 muci/cc to 592 +/- 5 muci/cc for GM. Both visual and quantitative assessments show improvement of partial volume correction in the synthesized and phantom experiments. CONCLUSIONS: The partial volume correction method improves the quantification of PET images. The method is comparable to the GTM method but does not need MR image segmentation or prior tracer distribution information. The voxel-based method can be particularly useful for combined PET/MRI studies. AD - Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia 30329, USA. AN - 22225287 AU - Wang, H. AU - Fei, B. C2 - 3261055 DA - Jan DO - 10.1118/1.3665704 [doi] DP - Nlm ET - 2012/01/10 IS - 1 KW - Algorithms Artifacts Brain/anatomy & histology/ diagnostic imaging Humans Image Enhancement/ methods Image Interpretation, Computer-Assisted/ methods Imaging, Three-Dimensional/ methods Magnetic Resonance Imaging/ methods Positron-Emission Tomography/ methods Reproducibility of Results Sensitivity and Specificity L1 - internal-pdf://4260411398/Wang-2012-An MR image-guided, voxel-based part.pdf LA - eng N1 - Wang, Hesheng Fei, Baowei R01CA156775/CA/NCI NIH HHS/United States R21 CA120536/CA/NCI NIH HHS/United States UL1 RR015008/RR/NCRR NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States P50CA128613/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States UL1 RR025008/RR/NCRR NIH HHS/United States P50 CA128301/CA/NCI NIH HHS/United States P50 CA128613/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't United States Medical physics Med Phys. 2012 Jan;39(1):179-95. doi: 10.1118/1.3665704. PY - 2012 SN - 0094-2405 (Print) 0094-2405 (Linking) SP - 179-95 ST - An MR image-guided, voxel-based partial volume correction method for PET images T2 - Med Phys TI - An MR image-guided, voxel-based partial volume correction method for PET images UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3261055/pdf/MPHYA6-000039-000179_1.pdf VL - 39 ID - 58 ER - TY - JOUR AB - PURPOSE: To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. METHODS: The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors' classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. RESULTS: The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors' automatic classification and manual segmentation were 91.6% +/- 2.0%. CONCLUSIONS: A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution. AD - Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30329, USA. AN - 23039675 AU - Yang, X. AU - Wu, S. AU - Sechopoulos, I. AU - Fei, B. C2 - 3477198 DA - Oct DO - 10.1118/1.4754654 [doi] DP - Nlm ET - 2012/10/09 IS - 10 KW - Artifacts Automation Breast/ cytology/pathology Humans Image Processing, Computer-Assisted/ methods Mammography/ methods Phantoms, Imaging Tomography, X-Ray Computed/ methods L1 - internal-pdf://0584433587/Yang-2012-Cupping artifact correction and auto.pdf LA - eng N1 - Yang, Xiaofeng Wu, Shengyong Sechopoulos, Ioannis Fei, Baowei R01CA156775/CA/NCI NIH HHS/United States R01CA163746/CA/NCI NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States P50CA128613/CA/NCI NIH HHS/United States UL1 TR000454/TR/NCATS NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States UL1 RR025008/RR/NCRR NIH HHS/United States P50 CA128301/CA/NCI NIH HHS/United States P50 CA128613/CA/NCI NIH HHS/United States R01 CA163746/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural United States Medical physics Med Phys. 2012 Oct;39(10):6397-406. doi: 10.1118/1.4754654. PY - 2012 SN - 0094-2405 (Print) 0094-2405 (Linking) SP - 6397-406 ST - Cupping artifact correction and automated classification for high-resolution dedicated breast CT images T2 - Med Phys TI - Cupping artifact correction and automated classification for high-resolution dedicated breast CT images UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3477198/pdf/MPHYA6-000039-006397_1.pdf VL - 39 ID - 51 ER - TY - JOUR AB - Multimodatity imaging is a promising approach for improving prostate cancer detection and diagnosis. This article describes various concepts in PET-directed, ultrasound-guided biopsies and highlights a new PET/ultrasound fusion targeted biopsy system for prostate cancer detection. AD - Department of Radiology and Imaging Sciences, Emory University, 1841 Clifton Road NE, Atlanta, GA 30329, USA. Department of Urology, Emory University, 1365 Clifton Road NE, Atlana, GA 30322. Department of Radiology and Imaging Sciences, Emory University, 1841 Clifton Road NE, Atlana, GA 30329. AN - 25392702 AU - Fei, B. AU - Nieh, P. T. AU - Schuster, D. M. AU - Master, V. A. C2 - 4225556 DA - Jan DP - Nlm ET - 2013/01/01 IS - 1 L1 - internal-pdf://2062232292/Fei-2013-PET-directed, 3D Ultrasound-guided pr.pdf LA - eng N1 - Fei, Baowei Nieh, Peter T Schuster, David M Master, Viraj A P50 CA128301/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States England Diagnostic imaging Europe Nihms514152 Diagn Imaging Eur. 2013 Jan;29(1):12-15. PY - 2013 SN - 1461-0051 (Print) 1461-0051 (Linking) SP - 12-15 ST - PET-directed, 3D Ultrasound-guided prostate biopsy T2 - Diagn Imaging Eur TI - PET-directed, 3D Ultrasound-guided prostate biopsy UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4225556/pdf/nihms514152.pdf VL - 29 ID - 49 ER - TY - JOUR AB - An automatic segmentation framework is proposed to segment the right ventricle (RV) in echocardiographic images. The method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform, a training model, and a localized region-based level set. First, the sparse matrix transform extracts main motion regions of the myocardium as eigen-images by analyzing the statistical information of the images. Second, an RV training model is registered to the eigen-images in order to locate the position of the RV. Third, the training model is adjusted and then serves as an optimized initialization for the segmentation of each image. Finally, based on the initializations, a localized, region-based level set algorithm is applied to segment both epicardial and endocardial boundaries in each echocardiograph. Three evaluation methods were used to validate the performance of the segmentation framework. The Dice coefficient measures the overall agreement between the manual and automatic segmentation. The absolute distance and the Hausdorff distance between the boundaries from manual and automatic segmentation were used to measure the accuracy of the segmentation. Ultrasound images of human subjects were used for validation. For the epicardial and endocardial boundaries, the Dice coefficients were 90.8 +/- 1.7% and 87.3 +/- 1.9%, the absolute distances were 2.0 +/- 0.42 mm and 1.79 +/- 0.45 mm, and the Hausdorff distances were 6.86 +/- 1.71 mm and 7.02 +/- 1.17 mm, respectively. The automatic segmentation method based on a sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging. AD - Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30329, USA. AN - 24107618 AU - Qin, X. AU - Cong, Z. AU - Fei, B. C2 - 3925785 DA - Nov 07 DO - 10.1088/0031-9155/58/21/7609 [doi] DP - Nlm ET - 2013/10/11 IS - 21 KW - Algorithms Automation Echocardiography/ methods Endocardium/diagnostic imaging Heart Ventricles/ diagnostic imaging Humans Image Processing, Computer-Assisted/ methods Pericardium/diagnostic imaging L1 - internal-pdf://3451944497/Qin-2013-Automatic segmentation of right ventr.pdf LA - eng N1 - Qin, Xulei Cong, Zhibin Fei, Baowei R01CA156775/CA/NCI NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States R21 CA176684/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States R21CA176684/CA/NCI NIH HHS/United States P50 CA128301/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't England Physics in medicine and biology Nihms539397 Phys Med Biol. 2013 Nov 7;58(21):7609-24. doi: 10.1088/0031-9155/58/21/7609. Epub 2013 Oct 10. PY - 2013 SN - 1361-6560 (Electronic) 0031-9155 (Linking) SP - 7609-24 ST - Automatic segmentation of right ventricular ultrasound images using sparse matrix transform and a level set T2 - Phys Med Biol TI - Automatic segmentation of right ventricular ultrasound images using sparse matrix transform and a level set UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3925785/pdf/nihms539397.pdf VL - 58 ID - 42 ER - TY - JOUR AB - Anti-1-amino-3-[(18)F] fluorocyclobutane-1-carboxylic acid (anti-3-[(18)F] FACBC) is a synthetic amino acid positron emission tomography (PET) radiotracer with utility in the detection of recurrent prostate carcinoma. The aim of this study is to correlate uptake of anti-3-[(18)F] FACBC with histology of prostatectomy specimens in patients undergoing radical prostatectomy and to determine if uptake correlates to markers of tumor aggressiveness such as Gleason score. Ten patients with prostate carcinoma pre-radical prostatectomy underwent 45 minute dynamic PET-CT of the pelvis after IV injection of 347.8 +/- 81.4 MBq anti-3-[(18)F] FACBC. Each prostate was co-registered to a separately acquired MR, divided into 12 sextants, and analyzed visually for abnormal focal uptake at 4, 16, 28, and 40 min post-injection by a single reader blinded to histology. SUVmax per sextant and total sextant activity (TSA) was also calculated. Histology and Gleason scores were similarly recorded by a urologic pathologist blinded to imaging. Imaging and histologic analysis were then compared. In addition, 3 representative sextants from each prostate were chosen based on highest, lowest and median SUVmax for immunohistochemical (IHC) analysis of Ki67, synaptophysin, P504s, chromogranin A, P53, androgen receptor, and prostein. 79 sextants had malignancy and 41 were benign. Highest combined sensitivity and specificity was at 28 min by visual analysis; 81.3% and 50.0% respectively. SUVmax was significantly higher (p<0.05) for malignant sextants (5.1+/-2.6 at 4 min; 4.5+/-1.6 at 16 min; 4.0+/-1.3 at 28 min; 3.8+/-1.0 at 40 min) compared to non-malignant sextants (4.0+/-1.9 at 4 min; 3.5+/-0.8 at 16 min; 3.4+/-0.9 at 28 min; 3.3+/-0.9 at 40 min), though there was overlap of activity between malignant and non-malignant sextants. SUVmax also significantly correlated (p<0.05) with Gleason score at all time points (r=0.28 at 4 min; r=0.42 at 16 min; r=0.46 at 28 min; r=0.48 at 40 min). There was no significant correlation of anti-3-[(18)F] FACBC SUVmax with Ki-67 or other IHC markers. Since there was no distinct separation between malignant and non-malignant sextants or between Gleason score levels, we believe that anti-3-[(18)F] FACBC PET should not be used alone for radiation therapy planning but may be useful to guide biopsy to the most aggressive lesion. AD - Department of Radiology and Imaging Sciences, Emory University Atlanta, GA, USA. AN - 23342303 AU - Schuster, D. M. AU - Taleghani, P. A. AU - Nieh, P. T. AU - Master, V. A. AU - Amzat, R. AU - Savir-Baruch, B. AU - Halkar, R. K. AU - Fox, T. AU - Osunkoya, A. O. AU - Moreno, C. S. AU - Nye, J. A. AU - Yu, W. AU - Fei, B. AU - Wang, Z. AU - Chen, Z. AU - Goodman, M. M. C2 - 3545368 DP - Nlm ET - 2013/01/24 IS - 1 L1 - internal-pdf://0133975347/Schuster-2013-Characterization of primary pros.pdf LA - eng N1 - Schuster, David M Taleghani, Pooneh A Nieh, Peter T Master, Viraj A Amzat, Rianot Savir-Baruch, Bital Halkar, Raghuveer K Fox, Tim Osunkoya, Adeboye O Moreno, Carlos S Nye, Jonathon A Yu, Weiping Fei, Baowei Wang, Zhibo Chen, Zhengjia Goodman, Mark M R01 CA156775/CA/NCI NIH HHS/United States United States American journal of nuclear medicine and molecular imaging Am J Nucl Med Mol Imaging. 2013;3(1):85-96. Epub 2013 Jan 5. PY - 2013 SN - 2160-8407 (Print) SP - 85-96 ST - Characterization of primary prostate carcinoma by anti-1-amino-2-[(18)F] -fluorocyclobutane-1-carboxylic acid (anti-3-[(18)F] FACBC) uptake T2 - Am J Nucl Med Mol Imaging TI - Characterization of primary prostate carcinoma by anti-1-amino-2-[(18)F] -fluorocyclobutane-1-carboxylic acid (anti-3-[(18)F] FACBC) uptake UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3545368/pdf/ajnmmi0003-0085.pdf VL - 3 ID - 47 ER - TY - JOUR AB - PURPOSE: To analyze the frequency domain characteristics of the signal in mammography images and breast tomosynthesis projections with patient tissue texture due to detected scattered x-rays. METHODS: Acquisitions of x-ray projection images of 19 different patient breasts were simulated using previously acquired volumetric patient images. Acquisition of these images was performed with a dedicated breast CT prototype system, and the images were classified into voxels representing skin, adipose, and glandular tissue with a previously validated automated algorithm. The classified three dimensional images then underwent simulated mechanical compression representing that which is performed during acquisition of mammography and breast tomosynthesis images. The acquisition of projection images of each patient breast was simulated using Monte Carlo methods with each simulation resulting in two images: one of the primary (non-scattered) signal and one of the scatter signal. To analyze the scatter signal for both mammography and breast tomosynthesis, two projections images of each patient breast were simulated, one with the x-ray source positioned at 0 degrees (mammography and central tomosynthesis projection) and at 30 degrees (wide tomosynthesis projection). The noise power spectra (NPS) for both the scatter signal alone and the total signal (primary + scatter) for all images were obtained and the combined results of all patients analyzed. The total NPS was fit to the expected power-law relationship NPS(f) = k/f beta and the results were compared with those previously published on the power spectrum characteristics of mammographic texture. The scatter signal alone was analyzed qualitatively and a power-law fit was also performed. RESULTS: The mammography and tomosynthesis projections of three patient breasts were too small to analyze, so a total of 16 patient breasts were analyzed. The values of beta for the total signal of the 0 degrees projections agreed well with previously published results. As expected, the scatter power spectrum reflected a fast drop-off with increasing spatial frequency, with a reduction of four orders of magnitude by 0.1 lp/mm. The beta values for the scatter signal were 6.14 and 6.39 for the 0 degrees and 30 degrees projections, respectively. CONCLUSIONS: Although the low-frequency characteristics of scatter in mammography and breast tomosynthesis were known, a quantitative analysis of the frequency domain characteristics of this signal was needed in order to optimize previously proposed software-based x-ray scatter reduction algorithms for these imaging modalities. AD - Departments of Radiology and Imaging Sciences, Hematology and Medical Oncology and Winship Cancer Institute, Emory University, 1701 Upper Gate Drive NE, Suite 5018, Atlanta, Georgia 30322. AN - 24089907 AU - Sechopoulos, I. AU - Bliznakova, K. AU - Fei, B. C2 - 3785536 DA - Oct DO - 10.1118/1.4820442 [doi] DP - Nlm ET - 2013/10/05 IS - 10 KW - Breast Humans Image Processing, Computer-Assisted/ methods Mammography/ methods Monte Carlo Method X-Ray Diffraction L1 - internal-pdf://1432523076/Sechopoulos-2013-Power spectrum analysis of th.pdf LA - eng N1 - Sechopoulos, Ioannis Bliznakova, Kristina Fei, Baowei R01CA156775/CA/NCI NIH HHS/United States R01CA163746/CA/NCI NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States R21 CA176684/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States P50 CA128301/CA/NCI NIH HHS/United States R01 CA163746/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't United States Medical physics Med Phys. 2013 Oct;40(10):101905. doi: 10.1118/1.4820442. PY - 2013 SN - 2473-4209 (Electronic) 0094-2405 (Linking) SP - 101905 ST - Power spectrum analysis of the x-ray scatter signal in mammography and breast tomosynthesis projections T2 - Med Phys TI - Power spectrum analysis of the x-ray scatter signal in mammography and breast tomosynthesis projections UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3785536/pdf/MPHYA6-000040-101905_1.pdf VL - 40 ID - 43 ER - TY - JOUR AB - A nonrigid B-spline-based point-matching (BPM) method is proposed to match dense surface points. The method solves both the point correspondence and nonrigid transformation without features extraction. The registration method integrates a motion model, which combines a global transformation and a B-spline-based local deformation, into a robust point-matching framework. The point correspondence and deformable transformation are estimated simultaneously by fuzzy correspondence and by a deterministic annealing technique. Prior information about global translation, rotation and scaling is incorporated into the optimization. A local B-spline motion model decreases the degrees of freedom for optimization and thus enables the registration of a larger number of feature points. The performance of the BPM method has been demonstrated and validated using synthesized 2D and 3D data, mouse MRI and micro-CT images. The proposed BPM method can be used to register feature point sets, 2D curves, 3D surfaces and various image data. AD - Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30329, USA. AN - 23732538 AU - Wang, H. AU - Fei, B. C2 - 3819195 DA - Jun 21 DO - 10.1088/0031-9155/58/12/4315 [doi] DP - Nlm ET - 2013/06/05 IS - 12 KW - Animals Imaging, Three-Dimensional/ methods Magnetic Resonance Imaging Mice Photochemotherapy Surface Properties Whole Body Imaging X-Ray Microtomography L1 - internal-pdf://3746099487/Wang-2013-Nonrigid point registration for 2D c.pdf LA - eng N1 - Wang, Hesheng Fei, Baowei P50 CA128301/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States R01CA156775/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't England Physics in medicine and biology Nihms491238 Phys Med Biol. 2013 Jun 21;58(12):4315-30. doi: 10.1088/0031-9155/58/12/4315. Epub 2013 Jun 4. PY - 2013 SN - 1361-6560 (Electronic) 0031-9155 (Linking) SP - 4315-30 ST - Nonrigid point registration for 2D curves and 3D surfaces and its various applications T2 - Phys Med Biol TI - Nonrigid point registration for 2D curves and 3D surfaces and its various applications UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3819195/pdf/nihms491238.pdf VL - 58 ID - 46 ER - TY - JOUR AB - BACKGROUND AND OBJECTIVE: Combined magnetic resonance/positron emission tomography (MR/PET) is a relatively new, hybrid imaging modality. MR-based attenuation correction often requires segmentation of the bone on MR images. In this study, we present an automatic segmentation method for the skull on MR images for attenuation correction in brain MR/PET applications. MATERIALS AND METHODS: Our method transforms T1-weighted MR images to the Radon domain and then detects the features of the skull image. In the Radon domain we use a bilateral filter to construct a multiscale image series. For the repeated convolution we increase the spatial smoothing in each scale and make the width of the spatial and range Gaussian function doubled in each scale. Two filters with different kernels along the vertical direction are applied along the scales from the coarse to fine levels. The results from a coarse scale give a mask for the next fine scale and supervise the segmentation in the next fine scale. The use of the multiscale bilateral filtering scheme is to improve the robustness of the method for noise MR images. After combining the two filtered sinograms, the reciprocal binary sinogram of the skull is obtained for the reconstruction of the skull image. RESULTS: This method has been tested with brain phantom data, simulated brain data, and real MRI data. For real MRI data the Dice overlap ratios are 92.2%+/-1.9% between our segmentation and manual segmentation. CONCLUSIONS: The multiscale segmentation method is robust and accurate and can be used for MRI-based attenuation correction in combined MR/PET. AD - Department of Radiology and Imaging Sciences, Center for Systems Imaging, Emory University, Atlanta, Georgia, USA. AN - 23761683 AU - Yang, X. AU - Fei, B. C2 - 3822115 DA - Nov-Dec DO - amiajnl-2012-001544 [pii] 10.1136/amiajnl-2012-001544 [doi] DP - Nlm ET - 2013/06/14 IS - 6 KW - Brain/diagnostic imaging/ pathology Diagnosis, Computer-Assisted Humans Magnetic Resonance Imaging/ methods Mathematical Concepts Phantoms, Imaging Positron-Emission Tomography/ methods Skull/diagnostic imaging/ pathology L1 - internal-pdf://2422163788/Yang-2013-Multiscale segmentation of the skull.pdf LA - eng N1 - Yang, Xiaofeng Fei, Baowei P50 CA128301/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States R01CA156775/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't England Journal of the American Medical Informatics Association : JAMIA J Am Med Inform Assoc. 2013 Nov-Dec;20(6):1037-45. doi: 10.1136/amiajnl-2012-001544. Epub 2013 Jun 12. PY - 2013 SN - 1527-974X (Electronic) 1067-5027 (Linking) SP - 1037-45 ST - Multiscale segmentation of the skull in MR images for MRI-based attenuation correction of combined MR/PET T2 - J Am Med Inform Assoc TI - Multiscale segmentation of the skull in MR images for MRI-based attenuation correction of combined MR/PET UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3822115/pdf/amiajnl-2012-001544.pdf VL - 20 ID - 45 ER - TY - JOUR AU - Zhen, Zipeng AU - Tang, Wei AU - Guo, Cunlan AU - Chen, Hongmin AU - Lin, Xin AU - Liu, Gang AU - Fei, Baowei AU - Chen, Xiaoyuan AU - Xu, Binqian AU - Xie, Jin IS - 8 L1 - internal-pdf://0717082850/Zhen-2013-Ferritin nanocages to encapsulate an.pdf PY - 2013 SN - 1936-0851 SP - 6988-6996 ST - Ferritin nanocages to encapsulate and deliver photosensitizers for efficient photodynamic therapy against cancer T2 - ACS nano TI - Ferritin nanocages to encapsulate and deliver photosensitizers for efficient photodynamic therapy against cancer UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3819164/pdf/nihms514164.pdf VL - 7 ID - 195 ER - TY - JOUR AB - BACKGROUND: Clinical studies show that metformin attenuates all-cause mortality and myocardial infarction compared with other medications for type 2 diabetes, even at similar glycemic levels. However, there is paucity of data in the euglycemic state on the vasculoprotective effects of metformin. The objectives of this study are to evaluate the effects of metformin on ameliorating atherosclerosis. METHODS AND RESULTS: Using ApoE-/- C57BL/6J mice, we found that metformin attenuates atherosclerosis and vascular senescence in mice fed a high-fat diet and prevents the upregulation of angiotensin II type 1 receptor by a high-fat diet in the aortas of mice. Thus, considering the known deleterious effects of angiotensin II mediated by angiotensin II type 1 receptor, the vascular benefits of metformin may be mediated, at least in part, by angiotensin II type 1 receptor downregulation. Moreover, we found that metformin can cause weight loss without hypoglycemia. We also found that metformin increases the antioxidant superoxide dismutase-1. CONCLUSION: Pleiotropic effects of metformin ameliorate atherosclerosis and vascular senescence. AD - Division of Cardiology, Emory University School of Medicine, Atlanta, GA AN - 25527624 AU - Forouzandeh, F. AU - Salazar, G. AU - Patrushev, N. AU - Xiong, S. AU - Hilenski, L. AU - Fei, B. AU - Alexander, R. W. C2 - 4338706 DA - Dec DO - jah3786 [pii] 10.1161/JAHA.114.001202 [doi] DP - Nlm ET - 2014/12/21 IS - 6 KW - Animals Aorta/drug effects/metabolism/pathology Aortic Diseases/genetics/metabolism/pathology/ prevention & control Apolipoproteins E/deficiency/genetics Atherosclerosis/genetics/metabolism/pathology/ prevention & control Blood Glucose/drug effects/metabolism Cardiovascular Agents/ pharmacology Cell Aging/drug effects Disease Models, Animal Hypoglycemic Agents/ pharmacology Male Metformin/ pharmacology Mice, Inbred C57BL Mice, Knockout Receptor, Angiotensin, Type 1/drug effects/metabolism Superoxide Dismutase/metabolism Superoxide Dismutase-1 Weight Loss/drug effects L1 - internal-pdf://3481139685/Forouzandeh-2014-Metformin beyond diabetes_ pl.pdf LA - eng N1 - Forouzandeh, Farshad Salazar, Gloria Patrushev, Nikolay Xiong, Shiqin Hilenski, Lula Fei, Baowei Alexander, R Wayne R01 CA156775/CA/NCI NIH HHS/United States R21 CA176684/CA/NCI NIH HHS/United States Research Support, Non-U.S. Gov't England Journal of the American Heart Association J Am Heart Assoc. 2014 Dec;3(6):e001202. doi: 10.1161/JAHA.114.001202. PY - 2014 SN - 2047-9980 (Electronic) 2047-9980 (Linking) SP - e001202 ST - Metformin beyond diabetes: pleiotropic benefits of metformin in attenuation of atherosclerosis T2 - J Am Heart Assoc TI - Metformin beyond diabetes: pleiotropic benefits of metformin in attenuation of atherosclerosis UR - http://jaha.ahajournals.org/content/ahaoa/3/6/e001202.full.pdf VL - 3 ID - 28 ER - TY - JOUR AU - Liu, Hong AU - Wang, Jie AU - Xu, Xiangyang AU - Song, Enmin AU - Wang, Qian AU - Jin, Renchao AU - Hung, Chih-Cheng AU - Fei, Baowei IS - 9 L1 - internal-pdf://0213060606/Liu-2014-A robust and accurate center-frequenc.pdf PY - 2014 SN - 0730-725X SP - 1139-1155 ST - A robust and accurate center-frequency estimation (RACE) algorithm for improving motion estimation performance of SinMod on tagged cardiac MR images without known tagging parameters T2 - Magnetic resonance imaging TI - A robust and accurate center-frequency estimation (RACE) algorithm for improving motion estimation performance of SinMod on tagged cardiac MR images without known tagging parameters UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4545264/pdf/nihms-716032.pdf VL - 32 ID - 213 ER - TY - JOUR AB - Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. AD - Emory University and Georgia Institute of Technology, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia 30322. Emory University and Georgia Institute of Technology, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia 30322bEmory University, School of Medicine, Department of Radiology and Imaging Sciences, Atlanta, Georgia 30329cEmory Univ. AN - 24441941 AU - Lu, G. AU - Fei, B. C2 - 3895860 DA - Jan DO - 1816617 [pii] 10.1117/1.JBO.19.1.010901 [doi] DP - Nlm ET - 2014/01/21 IS - 1 KW - Color Computers Diabetic Foot/diagnosis Diagnostic Imaging/ trends Fluorescent Dyes/chemistry Heart Diseases/diagnosis Humans Imaging, Three-Dimensional Light Metals/chemistry Neoplasms/diagnosis Neural Networks (Computer) Oxides/chemistry Retinal Diseases/diagnosis Semiconductors Shock/diagnosis Spectrophotometry/ methods Support Vector Machine Surface Properties Surgical Procedures, Operative L1 - internal-pdf://3216875164/Lu-2014-Medical hyperspectral imaging_ a revie.pdf LA - eng N1 - Lu, Guolan Fei, Baowei P50 CA128301/CA/NCI NIH HHS/United States P50 CA128613/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States R21 CA176684/CA/NCI NIH HHS/United States Review United States Journal of biomedical optics J Biomed Opt. 2014 Jan;19(1):10901. doi: 10.1117/1.JBO.19.1.010901. PY - 2014 SN - 1560-2281 (Electronic) 1083-3668 (Linking) SP - 10901 ST - Medical hyperspectral imaging: a review T2 - J Biomed Opt TI - Medical hyperspectral imaging: a review UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3895860/pdf/JBO-019-010901.pdf VL - 19 ID - 37 ER - TY - JOUR AB - Early detection of malignant lesions could improve both survival and quality of life of cancer patients. Hyperspectral imaging (HSI) has emerged as a powerful tool for noninvasive cancer detection and diagnosis, with the advantage of avoiding tissue biopsy and providing diagnostic signatures without the need of a contrast agent in real time. We developed a spectral-spatial classification method to distinguish cancer from normal tissue on hyperspectral images. We acquire hyperspectral reflectance images from 450 to 900 nm with a 2-nm increment from tumor-bearing mice. In our animal experiments, the HSI and classification method achieved a sensitivity of 93.7% and a specificity of 91.3%. The preliminary study demonstrated that HSI has the potential to be applied in vivo for noninvasive detection of tumors. AD - Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia 30329, United States. Emory University School of Medicine, Department of Radiology and Imaging Sciences, Atlanta, Georgia 30329, United States. Emory University School of Medicine, Department of Hematology and Medical Oncology, Atlanta, Georgia 30329, United States. Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia 30329, United StatesbEmory University School of Medicine, Department of Radiology and Imaging Sciences, Atlanta, Georgia 30. AN - 25277147 AU - Lu, G. AU - Halig, L. AU - Wang, D. AU - Qin, X. AU - Chen, Z. G. AU - Fei, B. C2 - 4183763 DO - 1915159 [pii] 10.1117/1.JBO.19.10.106004 [doi] DP - Nlm ET - 2014/10/04 IS - 10 KW - Animals Cell Line, Tumor Early Detection of Cancer/ methods Mice Neoplasms/chemistry/ diagnosis Optical Imaging/ methods Reproducibility of Results Sensitivity and Specificity Spectrum Analysis/ methods L1 - internal-pdf://2704376577/Lu-2014-Spectral-spatial classification for no.pdf LA - eng N1 - Lu, Guolan Halig, Luma Wang, Dongsheng Qin, Xulei Chen, Zhuo Georgia Fei, Baowei R01CA156775/CA/NCI NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States P50CA128613/CA/NCI NIH HHS/United States R21 CA176684/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States R21CA176684/CA/NCI NIH HHS/United States P50 CA128301/CA/NCI NIH HHS/United States P50 CA128613/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't United States Journal of biomedical optics J Biomed Opt. 2014;19(10):106004. doi: 10.1117/1.JBO.19.10.106004. PY - 2014 SN - 1560-2281 (Electronic) 1083-3668 (Linking) SP - 106004 ST - Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging T2 - J Biomed Opt TI - Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4183763/pdf/JBO-019-106004.pdf VL - 19 ID - 34 ER - TY - JOUR AU - Malliori, A AU - Bliznakova, K AU - Sechopoulos, I AU - Kamarianakis, Z AU - Fei, B AU - Pallikarakis, N IS - 16 L1 - internal-pdf://2809872362/Malliori-2014-Breast tomosynthesis with monoch.pdf PY - 2014 SN - 0031-9155 SP - 4681 ST - Breast tomosynthesis with monochromatic beams: a feasibility study using Monte Carlo simulations T2 - Physics in medicine and biology TI - Breast tomosynthesis with monochromatic beams: a feasibility study using Monte Carlo simulations UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4164851/pdf/nihms619064.pdf VL - 59 ID - 212 ER - TY - JOUR AB - High-frequency ultrasound (HFU) has the ability to image both skeletal and cardiac muscles. The quantitative assessment of these myofiber orientations has a number of applications in both research and clinical examinations; however, difficulties arise due to the severe speckle noise contained in the HFU images. Thus, for the purpose of automatically measuring myofiber orientations from two-dimensional HFU images, we propose a two-step multiscale image decomposition method. It combines a nonlinear anisotropic diffusion filter and a coherence enhancing diffusion filter to extract myofibers. This method has been verified by ultrasound data from simulated phantoms, excised fiber phantoms, specimens of porcine hearts, and human skeletal muscles in vivo. The quantitative evaluations of both phantoms indicated that the myofiber measurements of our proposed method were more accurate than other methods. The myofiber orientations extracted from different layers of the porcine hearts matched the prediction of an established cardiac mode and demonstrated the feasibility of extracting cardiac myofiber orientations from HFU images ex vivo. Moreover, HFU also demonstrated the ability to measure myofiber orientations in vivo. AD - Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30329, USA. AN - 24957945 AU - Qin, X. AU - Fei, B. C2 - 4137038 DA - Jul 21 DO - 10.1088/0031-9155/59/14/3907 [doi] DP - Nlm ET - 2014/06/25 IS - 14 KW - Animals Echocardiography/ methods Heart Ventricles/cytology Humans Image Processing, Computer-Assisted/ methods Myocardium/ cytology Phantoms, Imaging Swine L1 - internal-pdf://0619639211/Qin-2014-Measuring myofiber orientations from.pdf LA - eng N1 - Qin, Xulei Fei, Baowei R01CA156775/CA/NCI NIH HHS/United States R21 CA120536/CA/NCI NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States R21 CA176684/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States R21CA176684/CA/NCI NIH HHS/United States P50 CA128301/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't England Physics in medicine and biology Nihms610547 Phys Med Biol. 2014 Jul 21;59(14):3907-24. doi: 10.1088/0031-9155/59/14/3907. Epub 2014 Jun 24. PY - 2014 SN - 1361-6560 (Electronic) 0031-9155 (Linking) SP - 3907-24 ST - Measuring myofiber orientations from high-frequency ultrasound images using multiscale decompositions T2 - Phys Med Biol TI - Measuring myofiber orientations from high-frequency ultrasound images using multiscale decompositions UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4137038/pdf/nihms610547.pdf VL - 59 ID - 36 ER - TY - JOUR AU - Wang, Dongsheng AU - Fei, Baowei AU - Halig, Luma V AU - Qin, Xulei AU - Hu, Zhongliang AU - Xu, Hong AU - Wang, Yongqiang Andrew AU - Chen, Zhengjia AU - Kim, Sungjin AU - Shin, Dong M IS - 7 L1 - internal-pdf://2546639834/Wang-2014-Targeted iron-oxide nanoparticle for.pdf PY - 2014 SN - 1936-0851 SP - 6620-6632 ST - Targeted iron-oxide nanoparticle for photodynamic therapy and imaging of head and neck cancer T2 - ACS nano TI - Targeted iron-oxide nanoparticle for photodynamic therapy and imaging of head and neck cancer UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4155749/pdf/nn501652j.pdf VL - 8 ID - 210 ER - TY - JOUR AB - Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor. AD - Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia 30332, United States. Emory University, School of Medicine, Department of Hematology and Medical Oncology, , Atlanta, Georgia 30332, United States. Emory University, School of Medicine, Department of Radiology and Imaging Sciences, , Atlanta, Georgia 30332, United States. Emory University, School of Medicine, Department of Otolaryngology, , Atlanta, Georgia 30332, United States. Dartmouth College, Thayer School of Engineering, Hanover, New Hampshire 03755, United States. Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia 30332, United StatescEmory University, School of Medicine, Department of Radiology and Imaging Sciences, , Atlanta, Georgia. AN - 26720879 AU - Lu, G. AU - Wang, D. AU - Qin, X. AU - Halig, L. AU - Muller, S. AU - Zhang, H. AU - Chen, A. AU - Pogue, B. W. AU - Chen, Z. G. AU - Fei, B. C2 - 4691647 DO - 2479908 [pii] 10.1117/1.JBO.20.12.126012 [doi] DP - Nlm ET - 2016/01/01 IS - 12 KW - Algorithms Animals Carcinoma, Squamous Cell/pathology/surgery Fourier Analysis Green Fluorescent Proteins/metabolism Head and Neck Neoplasms/ pathology/ surgery Humans Image Processing, Computer-Assisted/ methods Mice Microscopy, Fluorescence Motion Necrosis Neoplasm Transplantation Surgery, Computer-Assisted L1 - internal-pdf://0251444063/Lu-2015-Framework for hyperspectral image proc.pdf LA - eng N1 - Lu, Guolan Wang, Dongsheng Qin, Xulei Halig, Luma Muller, Susan Zhang, Hongzheng Chen, Amy Pogue, Brian W Chen, Zhuo Georgia Fei, Baowei R01CA156775/CA/NCI NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States P50CA128613/CA/NCI NIH HHS/United States R21 CA176684/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States R21CA176684/CA/NCI NIH HHS/United States P50 CA128301/CA/NCI NIH HHS/United States P50 CA128613/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural United States Journal of biomedical optics J Biomed Opt. 2015;20(12):126012. doi: 10.1117/1.JBO.20.12.126012. PY - 2015 SN - 1560-2281 (Electronic) 1083-3668 (Linking) SP - 126012 ST - Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery T2 - J Biomed Opt TI - Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4691647/pdf/JBO-020-126012.pdf VL - 20 ID - 21 ER - TY - JOUR AB - PURPOSE: To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. METHODS: Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting model used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors' classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. RESULTS: Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. CONCLUSIONS: A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging. AD - Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329. Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 and Winship Cancer Institute of Emory University, Atlanta, Georgia 30322. Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30322; Department of Mathematics and Computer Science, Emory University, Atlanta, Georgia 30322; and Winship Cancer Institute of Emory University, Atlanta, Georgia 30322. AN - 26520712 AU - Pike, R. AU - Sechopoulos, I. AU - Fei, B. C2 - 4600081 DA - Nov DO - 10.1118/1.4931958 [doi] DP - Nlm ET - 2015/11/02 IS - 11 KW - Algorithms Breast Neoplasms/ diagnostic imaging Computer Simulation Female Humans Imaging, Three-Dimensional/methods Mammography/ methods Models, Statistical Pattern Recognition, Automated/ methods Radiographic Image Enhancement/methods Radiographic Image Interpretation, Computer-Assisted/ methods Reproducibility of Results Sensitivity and Specificity Tomography, X-Ray Computed/ methods L1 - internal-pdf://2451396401/Pike-2015-A minimum spanning forest based clas.pdf LA - eng N1 - Pike, Robert Sechopoulos, Ioannis Fei, Baowei R01CA156775/CA/NCI NIH HHS/United States R01CA163746/CA/NCI NIH HHS/United States P50CA128301/CA/NCI NIH HHS/United States P50CA128613/CA/NCI NIH HHS/United States R21 CA176684/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States R21CA176684/CA/NCI NIH HHS/United States UL1 RR025008/RR/NCRR NIH HHS/United States P50 CA128301/CA/NCI NIH HHS/United States P50 CA128613/CA/NCI NIH HHS/United States R01 CA163746/CA/NCI NIH HHS/United States Evaluation Studies Research Support, N.I.H., Extramural Validation Studies United States Medical physics Med Phys. 2015 Nov;42(11):6190-202. doi: 10.1118/1.4931958. PY - 2015 SN - 2473-4209 (Electronic) 0094-2405 (Linking) SP - 6190-202 ST - A minimum spanning forest based classification method for dedicated breast CT images T2 - Med Phys TI - A minimum spanning forest based classification method for dedicated breast CT images UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4600081/pdf/MPHYA6-000042-006190_1.pdf VL - 42 ID - 24 ER - TY - JOUR AU - Qin, Xulei AU - Fei, Baowei IS - 6 L1 - internal-pdf://1879796339/Qin-2015-DTI template‐based estimation of card.pdf PY - 2015 SN - 2473-4209 SP - 2915-2924 ST - DTI template‐based estimation of cardiac fiber orientations from 3D ultrasound T2 - Medical physics TI - DTI template‐based estimation of cardiac fiber orientations from 3D ultrasound UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4441706/pdf/MPHYA6-000042-002915_1.pdf VL - 42 ID - 227 ER - TY - JOUR AB - PURPOSE: Cardiac ultrasound simulation can have important applications in the design of ultrasound systems, understanding the interaction effect between ultrasound and tissue and setting the ground truth for validating quantification methods. Current ultrasound simulation methods fail to simulate the myocardial intensity anisotropies. New simulation methods are needed in order to simulate realistic ultrasound images of the heart. METHODS: The proposed cardiac ultrasound image simulation method is based on diffusion tensor imaging (DTI) data of the heart. The method utilizes both the cardiac geometry and the fiber orientation information to simulate the anisotropic intensities in B-mode ultrasound images. Before the simulation procedure, the geometry and fiber orientations of the heart are obtained from high-resolution structural MRI and DTI data, respectively. The simulation includes two important steps. First, the backscatter coefficients of the point scatterers inside the myocardium are processed according to the fiber orientations using an anisotropic model. Second, the cardiac ultrasound images are simulated with anisotropic myocardial intensities. The proposed method was also compared with two other nonanisotropic intensity methods using 50 B-mode ultrasound image volumes of five different rat hearts. The simulated images were also compared with the ultrasound images of a diseased rat heart in vivo. A new segmental evaluation method is proposed to validate the simulation results. The average relative errors (AREs) of five parameters, i.e., mean intensity, Rayleigh distribution parameter sigma, and first, second, and third quartiles, were utilized as the evaluation metrics. The simulated images were quantitatively compared with real ultrasound images in both ex vivo and in vivo experiments. RESULTS: The proposed ultrasound image simulation method can realistically simulate cardiac ultrasound images of the heart using high-resolution MR-DTI data. The AREs of their proposed method are 19% for the mean intensity, 17.7% for the scale parameter of Rayleigh distribution, 36.8% for the first quartile of the image intensities, 25.2% for the second quartile, and 19.9% for the third quartile. In contrast, the errors of the other two methods are generally five times more than those of their proposed method. CONCLUSIONS: The proposed simulation method uses MR-DTI data and realistically generates cardiac ultrasound images with anisotropic intensities inside the myocardium. The ultrasound simulation method could provide a tool for many potential research and clinical applications in cardiac ultrasound imaging. AD - Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329. Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, Georgia 30329. Emory University Department of Pediatrics and Children's Healthcare of Atlanta, Atlanta, Georgia 30322. Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30329. Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329; Department of Mathematics and Computer Science, Emory University, Atlanta, Georgia 30329; and Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30329. AN - 26328966 AU - Qin, X. AU - Wang, S. AU - Shen, M. AU - Lu, G. AU - Zhang, X. AU - Wagner, M. B. AU - Fei, B. C2 - 4537486 DA - Sep DO - 10.1118/1.4927788 [doi] DP - Nlm ET - 2015/09/04 IS - 9 KW - Animals Anisotropy Diffusion Tensor Imaging Echocardiography Humans Magnetic Resonance Imaging Models, Biological Phantoms, Imaging Rats L1 - internal-pdf://3066612208/Qin-2015-Simulating cardiac ultrasound image b.pdf LA - eng N1 - Qin, Xulei Wang, Silun Shen, Ming Lu, Guolan Zhang, Xiaodong Wagner, Mary B Fei, Baowei R01CA156775/CA/NCI NIH HHS/United States P51 OD011132/OD/NIH HHS/United States R21 CA176684/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States R21CA176684/CA/NCI NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't United States Medical physics Med Phys. 2015 Sep;42(9):5144-56. doi: 10.1118/1.4927788. PY - 2015 SN - 2473-4209 (Electronic) 0094-2405 (Linking) SP - 5144-56 ST - Simulating cardiac ultrasound image based on MR diffusion tensor imaging T2 - Med Phys TI - Simulating cardiac ultrasound image based on MR diffusion tensor imaging UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4537486/pdf/MPHYA6-000042-005144_1.pdf VL - 42 ID - 26 ER - TY - JOUR AB - Accurate segmentation of the prostate has many applications in prostate cancer diagnosis and therapy. In this paper, we propose a "Supervoxel" based method for prostate segmentation. The prostate segmentation problem is considered as assigning a label to each supervoxel. An energy function with data and smoothness terms is used to model the labeling process. The data term estimates the likelihood of a supervoxel belongs to the prostate according to a shape feature. The geometric relationship between two neighboring supervoxels is used to construct a smoothness term. A three-dimensional (3D) graph cut method is used to minimize the energy function in order to segment the prostate. A 3D level set is then used to get a smooth surface based on the output of the graph cut. The performance of the proposed segmentation algorithm was evaluated with respect to the manual segmentation ground truth. The experimental results on 12 prostate volumes showed that the proposed algorithm yields a mean Dice similarity coefficient of 86.9%+/-3.2%. The segmentation method can be used not only for the prostate but also for other organs. AD - Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA. Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology. AN - 26848206 AU - Tian, Z. AU - Liu, L. AU - Fei, B. C2 - 4736748 DA - Mar 20 DO - 10.1117/12.2082255 [doi] DP - Nlm ET - 2016/02/06 LA - eng N1 - Tian, Zhiqiang Liu, LiZhi Fei, Baowei P50 CA128301/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States R21 CA176684/CA/NCI NIH HHS/United States United States Proceedings of SPIE--the International Society for Optical Engineering Nihms716021 Proc SPIE Int Soc Opt Eng. 2015 Mar 20;9413. pii: 941318. PY - 2015 SN - 0277-786X (Print) 0277-786X (Linking) ST - A supervoxel-based segmentation method for prostate MR images T2 - Proc SPIE Int Soc Opt Eng TI - A supervoxel-based segmentation method for prostate MR images UR - https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9413/1/A-supervoxel-based-segmentation-method-for-prostate-MR-images/10.1117/12.2082255.short VL - 9413 ID - 19 ER - TY - JOUR AB - One in six men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and magnetic resonance spectroscopy imaging. Because of the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. To improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. AD - Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329; Center of Medical Imaging and Image-guided Therapy, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, China. Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329. Center of Medical Imaging and Image-guided Therapy, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, China. Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, 1841 Clifton Road NE, Atlanta, Georgia 30329; Winship Cancer Institute of Emory University, 1841 Clifton Road NE, Atlanta, Georgia 30329. Electronic address: bfei@emory.edu. AN - 27133005 AU - Liu, L. AU - Tian, Z. AU - Zhang, Z. AU - Fei, B. C2 - 5355004 DA - Aug DO - S1076-6332(16)30009-5 [pii] 10.1016/j.acra.2016.03.010 [doi] DP - Nlm ET - 2016/05/03 IS - 8 L1 - internal-pdf://1009589971/Liu-2016-Computer-aided Detection of Prostate.pdf LA - eng N1 - Liu, Lizhi Tian, Zhiqiang Zhang, Zhenfeng Fei, Baowei P50 CA128301/CA/NCI NIH HHS/United States R01 CA156775/CA/NCI NIH HHS/United States R21 CA176684/CA/NCI NIH HHS/United States Review United States Academic radiology Nihms776473 Acad Radiol. 2016 Aug;23(8):1024-46. doi: 10.1016/j.acra.2016.03.010. Epub 2016 Apr 25. PY - 2016 SN - 1878-4046 (Electronic) 1076-6332 (Linking) SP - 1024-46 ST - Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications T2 - Acad Radiol TI - Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5355004/pdf/nihms776473.pdf VL - 23 ID - 16 ER - TY - JOUR AU - Pike, Robert AU - Lu, Guolan AU - Wang, Dongsheng AU - Chen, Zhuo Georgia AU - Fei, Baowei IS - 3 L1 - internal-pdf://1705455244/Pike-2016-A minimum spanning forest-based meth.pdf PY - 2016 SN - 0018-9294 SP - 653-663 ST - A minimum spanning forest-based method for noninvasive cancer detection with hyperspectral imaging T2 - IEEE Transactions on Biomedical Engineering TI - A minimum spanning forest-based method for noninvasive cancer detection with hyperspectral imaging UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4791052/pdf/nihms763459.pdf VL - 63 ID - 231 ER - TY - JOUR AU - Fei, Baowei AU - Nieh, Peter T AU - Master, Viraj A AU - Zhang, Yun AU - Osunkoya, Adeboye O AU - Schuster, David M PY - 2017 SN - 2281-5872 SP - 1-15 ST - Molecular imaging and fusion targeted biopsy of the prostate T2 - Clinical and Translational Imaging TI - Molecular imaging and fusion targeted biopsy of the prostate ID - 244 ER - TY - JOUR AU - Fei, Baowei W AU - Schuster, David M PY - 2017 SN - 0361-803X SP - 1-15 ST - PET Molecular Imaging–Directed Biopsy: A Review T2 - American Journal of Roentgenology TI - PET Molecular Imaging–Directed Biopsy: A Review ID - 282 ER - TY - JOUR AB - Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue-labeling of surgical specimens of head and neck patients. AD - Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United StatesbMedical College of Georgia, Augusta, Georgia, United States. Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States. Emory University School of Medicine, Department of Pathology and Laboratory Medicine, Atlanta, Georgia, United States. Emory University School of Medicine, Department of Hematology and Medical Oncology, Atlanta, Georgia, United States. Emory University School of Medicine, Department of Otolaryngology, Atlanta, Georgia, United StatesfWinship Cancer Institute of Emory University, Atlanta, Georgia, United States. Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United StatesfWinship Cancer Institute of Emory University, Atlanta, Georgia, United StatesgEmory University, Department of Radiology and Imaging Sciences, Atlanta, Georgia, United StateshEmory University, Department of Mathematics and Computer Science, Atlanta, Georgia, United States. AN - 28655055 AU - Halicek, M. AU - Lu, G. AU - Little, J. V. AU - Wang, X. AU - Patel, M. AU - Griffith, C. C. AU - El-Deiry, M. W. AU - Chen, A. Y. AU - Fei, B. C2 - 5482930 DA - Jun 01 DO - 2635785 [pii] 10.1117/1.JBO.22.6.060503 [doi] DP - Nlm ET - 2017/06/28 IS - 6 L1 - internal-pdf://4070055884/Halicek-2017-Deep convolutional neural network.pdf LA - eng N1 - Halicek, Martin Lu, Guolan Little, James V Wang, Xu Patel, Mihir Griffith, Christopher C El-Deiry, Mark W Chen, Amy Y Fei, Baowei United States Journal of biomedical optics J Biomed Opt. 2017 Jun 1;22(6):60503. doi: 10.1117/1.JBO.22.6.060503. PY - 2017 SN - 1560-2281 (Electronic) 1083-3668 (Linking) SP - 60503 ST - Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging T2 - J Biomed Opt TI - Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging UR - https://www.spiedigitallibrary.org/journalArticle/Download?fileName=060503 VL - 22 ID - 3 ER - TY - JOUR AU - Lu, Guolan AU - Little, James V AU - Wang, Xu AU - Griffith, Christopher C AU - El-Deiry, Mark AU - Chen, Amy Y AU - Fei, Baowei PY - 2017 SN - 1078-0432 SP - clincanres. 0906.2017 ST - Detection of Head and Neck Cancer in Surgical Specimens Using Quantitative Hyperspectral Imaging T2 - Clinical Cancer Research TI - Detection of Head and Neck Cancer in Surgical Specimens Using Quantitative Hyperspectral Imaging ID - 287 ER - TY - JOUR AU - Ma, Ling AU - Guo, Rongrong AU - Tian, Zhiqiang AU - Fei, Baowei PY - 2017 SN - 2473-4209 ST - A Random Walk‐based Segmentation Framework for 3D Ultrasound Images of the Prostate T2 - Medical Physics TI - A Random Walk‐based Segmentation Framework for 3D Ultrasound Images of the Prostate ID - 286 ER - TY - JOUR AB - PURPOSE: Segmentation of the prostate on CT images has many applications in the diagnosis and treatment of prostate cancer. Because of the low soft-tissue contrast on CT images, prostate segmentation is a challenging task. A learning-based segmentation method is proposed for the prostate on three-dimensional (3D) CT images. METHODS: We combine population-based and patient-based learning methods for segmenting the prostate on CT images. Population data can provide useful information to guide the segmentation processing. Because of inter-patient variations, patient-specific information is particularly useful to improve the segmentation accuracy for an individual patient. In this study, we combine a population learning method and a patient-specific learning method to improve the robustness of prostate segmentation on CT images. We train a population model based on the data from a group of prostate patients. We also train a patient-specific model based on the data of the individual patient and incorporate the information as marked by the user interaction into the segmentation processing. We calculate the similarity between the two models in order to obtain applicable population and patient-specific knowledge to compute the likelihood of a pixel belonging to the prostate tissue. A new adaptive threshold method is developed to convert the likelihood image into a binary image of the prostate, and thus complete the segmentation of the gland on CT images. RESULTS: The proposed learning-based segmentation algorithm was validated using 3D CT volumes of 92 patients. All of the CT image volumes were manually segmented independently three times by two, clinically experienced radiologists and the manual segmentation results served as the gold standard for evaluation. The experimental results show that the segmentation method achieved a Dice similarity coefficient of 87.18+/-2.99%, compared to the manual segmentation. CONCLUSIONS: By combining the population learning and patient-specific learning methods, the proposed method is effective for segmenting the prostate on 3D CT images. The prostate CT segmentation method can be used in various applications including volume measurement and treatment planning of the prostate. This article is protected by copyright. All rights reserved. AD - Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA. Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA. Winship Cancer Institute of Emory University, Atlanta, GA. Department of Mathematics and Computer Science, Emory University, Atlanta, GA. AN - 28834585 AU - Ma, L. AU - Guo, R. AU - Zhang, G. AU - Schuster, D. S. AU - Fei, B. DA - Aug 23 DO - 10.1002/mp.12528 [doi] DP - Nlm ET - 2017/08/24 LA - eng N1 - Ma, Ling Guo, Rongrong Zhang, Guoyi Schuster, David S Fei, Baowei United States Medical physics Med Phys. 2017 Aug 23. doi: 10.1002/mp.12528. PY - 2017 SN - 2473-4209 (Electronic) 0094-2405 (Linking) ST - A Combined Learning Algorithm for Prostate Segmentation on 3D CT Images T2 - Med Phys TI - A Combined Learning Algorithm for Prostate Segmentation on 3D CT Images UR - http://onlinelibrary.wiley.com/doi/10.1002/mp.12528/abstract ID - 2 ER - TY - JOUR AB - Common CT imaging signs of lung diseases (CISLs) are defined as the imaging signs that frequently appear in lung CT images from patients. CISLs play important roles in the diagnosis of lung diseases. This paper proposes a novel learning method, namely learning with distribution of optimized feature (DOF), to effectively recognize the characteristics of CISLs. We improve the classification performance by learning the optimized features under different distributions. Specifically, we adopt the minimum spanning tree algorithm to capture the relationship between features and discriminant ability of features for selecting the most important features. To overcome the problem of various distributions in one CISL, we propose a hierarchical learning method. First, we use an unsupervised learning method to cluster samples into groups based on their distribution. Second, in each group, we use a supervised learning method to train a model based on their categories of CISLs. Finally, we obtain multiple classification decisions from multiple trained models and use majority voting to achieve the final decision. The proposed approach has been implemented on a set of 511 samples captured from human lung CT images and achieves a classification accuracy of 91.96%. The proposed DOF method is effective and can provide a useful tool for computer-aided diagnosis of lung diseases on CT images. AD - Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA. School of Computer Science, Beijing Institute of Technology, Beijing, People's Republic of China. AN - 28033116 AU - Ma, L. AU - Liu, X. AU - Fei, B. DA - Jan 21 DO - 10.1088/1361-6560/62/2/612 [doi] DP - Nlm ET - 2016/12/30 IS - 2 LA - eng N1 - Ma, Ling Liu, Xiabi Fei, Baowei R01 CA156775/CA/NCI NIH HHS/United States R01 CA204254/CA/NCI NIH HHS/United States R21 CA176684/CA/NCI NIH HHS/United States England Physics in medicine and biology Phys Med Biol. 2017 Jan 21;62(2):612-632. doi: 10.1088/1361-6560/62/2/612. Epub 2016 Dec 29. PY - 2017 SN - 1361-6560 (Electronic) 0031-9155 (Linking) SP - 612-632 ST - Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases T2 - Phys Med Biol TI - Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases UR - http://iopscience.iop.org/article/10.1088/1361-6560/62/2/612/meta VL - 62 ID - 8 ER - TY - JOUR AB - PURPOSE: To characterize the dependence of normalized glandular dose (DgN) on various breast model and image acquisition parameters during spot compression mammography and other partial breast irradiation conditions, and evaluate alternative previously proposed dose-related metrics for this breast imaging modality. METHODS: Using Monte Carlo simulations with both simple homogeneous breast models and patient-specific breasts, three different dose-related metrics for spot compression mammography were compared: the standard DgN, the normalized glandular dose to only the directly irradiated portion of the breast (DgNv), and the DgN obtained by the product of the DgN for full field irradiation and the ratio of the mid-height area of the irradiated breast to the entire breast area (DgNM ). How these metrics vary with field-of-view size, spot area thickness, x-ray energy, spot area and position, breast shape and size, and system geometry was characterized for the simple breast model and a comparison of the simple model results to those with patient-specific breasts was also performed. RESULTS: The DgN in spot compression mammography can vary considerably with breast area. However, the difference in breast thickness between the spot compressed area and the uncompressed area does not introduce a variation in DgN. As long as the spot compressed area is completely within the breast area and only the compressed breast portion is directly irradiated, its position and size does not introduce a variation in DgN for the homogeneous breast model. As expected, DgN is lower than DgNv for all partial breast irradiation areas, especially when considering spot compression areas within the clinically used range. DgNM underestimates DgN by 6.7% for a W/Rh spectrum at 28 kVp and for a 9 x 9 cm2 compression paddle. CONCLUSION: As part of the development of a new breast dosimetry model, a task undertaken by the American Association of Physicists in Medicine and the European Federation of Organizations of Medical Physics, these results provide insight on how DgN and two alternative dose metrics behave with various image acquisition and model parameters. AD - Dipartimento di Fisica "Ettore Pancini", Universita di Napoli Federico II, Via Cintia, I-80126, Napoli, Italy. INFN Sezione di Napoli, I-80126, Napoli, Italy. National Co-ordinating Centre for the Physics of Mammography (NCCPM), Royal Surrey County Hospital, Guildford, GU2 7XX, UK. Department of Physics, University of Surrey, Guildford, GU2 7XH, UK. Dutch Reference Centre for Screening (LRCB), P.O. Box 6873, 6503 GJ, Nijmegen, The Netherlands. Department of Electronics, Technical University of Varna, 1 Studentska Str, Varna, 9010, Bulgaria. Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1701 Upper Gate Drive Northeast, Suite 5018, Atlanta, GA, 30322, USA. Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, USA. Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands. AN - 28500759 AU - Sarno, A. AU - Dance, D. R. AU - van Engen, R. E. AU - Young, K. C. AU - Russo, P. AU - Di Lillo, F. AU - Mettivier, G. AU - Bliznakova, K. AU - Fei, B. AU - Sechopoulos, I. C2 - 5534220 DA - Jul DO - 10.1002/mp.12339 [doi] DP - Nlm ET - 2017/05/14 IS - 7 LA - eng N1 - Sarno, Antonio Dance, David R van Engen, Ruben E Young, Kenneth C Russo, Paolo Di Lillo, Francesca Mettivier, Giovanni Bliznakova, Kristina Fei, Baowei Sechopoulos, Ioannis R01 CA156775/CA/NCI NIH HHS/United States R01 CA163746/CA/NCI NIH HHS/United States R01 CA181171/CA/NCI NIH HHS/United States R21 CA176684/CA/NCI NIH HHS/United States United States Medical physics Nihms876059 Med Phys. 2017 Jul;44(7):3848-3860. doi: 10.1002/mp.12339. Epub 2017 Jun 13. PY - 2017 SN - 2473-4209 (Electronic) 0094-2405 (Linking) SP - 3848-3860 ST - A Monte Carlo model for mean glandular dose evaluation in spot compression mammography T2 - Med Phys TI - A Monte Carlo model for mean glandular dose evaluation in spot compression mammography UR - http://onlinelibrary.wiley.com/doi/10.1002/mp.12339/abstract VL - 44 ID - 7 ER - TY - JOUR AU - Tian, Zhiqiang AU - Liu, LiZhi AU - Zhang, Zhenfeng AU - Xue, Jianru AU - Fei, Baowei IS - 2 PY - 2017 SN - 2473-4209 SP - 558-569 ST - A supervoxel‐based segmentation method for prostate MR images T2 - Medical physics TI - A supervoxel‐based segmentation method for prostate MR images UR - http://onlinelibrary.wiley.com/doi/10.1002/mp.12048/abstract VL - 44 ID - 226 ER -