Dr. Baowei Fei serves as the Chair for the NIH Study Section: ZCA1 RPRB-N (J2) Integrating Biospecimens into Clinical Assay Development (U01)

This Funding Opportunity Announcement (FOA) will support extramural research to investigate and mitigate challenges facing clinical assay development due to biopsy biospecimen preanalytical variability. The program will tie in with current efforts to optimize clinical biomarker assays utilized in NCI-sponsored clinical trials. Results from this research program will improve the understanding of how biopsy collection, processing, and storage procedures may affect all aspects of analytical performance for current and emerging clinical biomarkers, as well as expedite clinical biomarker assay development through the evidence-based standardization of biopsy handling practices. Critical information gained through these research awards may increase the reliability of clinical biomarker assays, reduce time requirements for assay development, and decrease assay failure during late-stage testing.

Dr. Baowei Fei serves as a Panel Member of the NIH Study Section: Biomedical Imaging Technology (BMIT-B)

The Biomedical Imaging Technology Study Sections both review applications involving basic, applied, and pre-clinical aspects of the design and development of medical imaging system technologies, their components, software, and mathematical methods for studies at the cellular, organ, small or large animal, and human scale.

Dr. Baowei Fei was appointed as Cecil H. and Ida Green Chair in Systems Biology Science by the University of Texas at Dallas

Provost Musselman of the University of Texas at Dallas appointed Dr. Baowei Fei to the endowed faculty position Cecil H. and Ida Green Chair in Systems Biology Science #3, one of the honorific appointment awarded by the University to its most distinguished faculty members.

Dr. Fei received a new NIH R01 grant on image-guided intravascular robotic system for mitral valve repair and implants.

Mitral regurgitation (MR) is one of the most common valve lesions, which affects 9 million Americans, and is known to increase morbidity and mortality. MR occurs due to leakage of blood through the mitral valve and induces volume overload on the left ventricle, elevates diastolic wall stress and causes rapid left ventricular dilatation, ultimately leading to congestive heart failure within 5 years and death. Timely and effective repair of MR is of utmost importance to halt the progression of heart failure, but current options are limited. Open- heart surgery is the current standard of care and has a relatively high risk of post-operative mortality. Transcatheter mitral valve repair, is a new class of technologies in which MR repair is performed on a beating heart using a catheter that is guided to the mitral valve to deploy reparative devices. However, the route to the mitral valve is a challenging path for existing catheters to follow. The complexity associated with their implantation in a beating heart, often leads to failed procedures and conversion to open heart surgery. We propose to develop a novel intravascular steerable robot that is guided to the mitral valve by multimodality imaging and deploys a novel, low profile device that can effectively repair MR of all forms. This highly innovative and interdisciplinary project combines expertise in surgical robotics, imaging and mitral repair devices. We envision that the intravascular steerable robot and implant, guided by multimodality imaging will significantly simplify Transcatheter mitral valve repair, increasing the procedural accuracy and control, and reducing failure rates.

Martin Halicek, an MD/PhD student in Dr. Fei’s Lab received the Robert Jones Award

The Jones Award creates opportunities for advancement in patient care and the discovery of cures. Through a unique collaboration between Emory University and Georgia Institute of Technology, the Jones Biomedical Engineering Fellows conduct groundbreaking research and lead the fight against debilitating diseases like Parkinson’s disease, Alzheimer’s disease, cancer, and syringomyelia, the rare neurological condition Jones suffered from at the end of his life.  The Jones Award is to recognize a BME student in the Laney Graduate School at Emory University for exceptional research accomplishments as identified by the faculty.

Dr. Baowei Fei served on the NIH Study Section ZRG1 SBIB-F (59)R on Imaging and Biomarker for Early Detection of Aggressive Cancer

The purpose of this Funding Opportunity Announcement (FOA) is to: (i) invite researchers to submit collaborative research project (U01) applications to improve cancer screening, early detection of aggressive cancer, assessment of cancer risk and cancer diagnosis aimed at integrating multi-modality imaging strategies and multiplexed biomarker methodologies into a singular complementary approach, and (ii) establish a Consortium for Imaging and Biomarkers (CIB) to perform collaborative studies, exchange information, share knowledge and leverage common resources. The research will be conducted by individual multi-disciplinary research teams, hereafter called Units. All Units are expected to participate in collaborative activities with other Units within the Consortium.

For more information about this funding program, please visit  NIH Grants.

Dr. Baowei Fei was awarded a five-year NIH R01 grant

The project is to translate our positron emission tomography (PET)/transrectal ultrasound (TRUS) fusion guided technology into a commercially supported platform for improving the detection of prostate cancer. It has been reported that the long-term prostate cancer specific survival of patients initially managed with active surveillance (AS) or watchful waiting for low-risk prostate cancer ranges from 97% to 100%. However, among all men with indolent prostate cancer, the rate of aggressive treatment is as high as 64.3%. The costs for the treatment are $12 billion each year in the USA. One reason for aggressive treatment is due to the fact that the current standard diagnosis with transrectal ultrasound-guided biopsy can miss up to 30% of cancers. A major concern for active surveillance is the risk of high-grade cancer that may be missed by the current diagnosis. This research is to develop innovative imaging technology that can improve the detection rate and distinguish aggressive cancer, which requires treatment, from the non-aggressive disease, which can be well-managed with active surveillance. The technology will provide clinicians a new imaging tool to select millions of low-risk prostate cancer patients for active surveillance instead of unnecessary treatment, therefore may help save billions of dollars in treatment costs and improve the care of prostate cancer patients.

Workshop and Launch of the Integrative Cancer Imaging Research Program (iCIRP)

The Integrative Cancer Imaging Research Program (iCIRP) is a joint program between Emory University School of Medicine Department of Radiology and Imaging Sciences, Georgia Tech/Emory Coulter Department of Biomedical Engineering, and Winship Cancer Institute of Emory University. This program will build on and synergize unique strengths inherent in Emory units and centers that foster multidisciplinary collaborations within and among the disciplines of imaging science, cancer biology, nanotechnology, biomarker development, computation, and clinical cancer research. The overarching goal of the iCIRP program is to advance cancer detection, diagnosis, prognosis, image-guided therapy, prediction of efficacy, and monitoring of treatment.

Dr. Baowei Fei’s talk on the introduction of the Program: PDF File

For more information about this program, please visit: http://radiology.emory.edu/research/icirp/index.html

Dr. Baowei Fei served as Chair for NIH Study Section ZRG1 SBIB-F (56)R on Early Phase Clinical Trials in Imaging and Image-guided Interventions

This Funding Opportunity Announcement (FOA) is intended to support clinical trials conducting preliminary evaluation of the safety and efficacy of imaging agents, as well as an assessment of imaging systems, image processing, image-guided therapy, contrast kinetic modeling, 3-D reconstruction and other quantitative tools. As many such preliminary evaluations are early in development, this FOA will provide investigators with support for pilot (Phase I and II) cancer imaging clinical trials, including patient monitoring and laboratory studies. This FOA supports novel uses of known/standard clinical imaging agents and methods as well as the evaluation of new agents, systems, or methods. The imaging and image-guided intervention (IGI) investigations, if proven successful in these early clinical trials, can then be validated in larger studies through competitive R01 mechanisms, or through clinical trials in the Specialized Programs of Research Excellence (SPOREs), Cancer Centers and/or the NCI's National Clinical Trials Network. October 21, 2016

For more information about this program, please visit NIH Grants.

Guolan Lu successfully defended her PhD thesis and joined Stanford University

As an emerging optical modality, hyperspectral imaging (HSI) holds great promise for early cancer detection and image-guided surgery. The major advantage of HSI is that it is a noninvasive technology that doesn't require any contrast agent, and it combines wide-field imaging and spectroscopy to simultaneously attain both spatial and spectral information from an object in a non-contact way. Light delivered to the tissue surface undergoes multiple elastic scattering and absorption interactions, and part of it returns as diffuse reflectance carrying diagnostic information about the underlying tissue structure and composition. The biochemical and morphological properties of the tissue change during disease progression. Therefore hyperspectral images, which contain high-dimensional spectral information at each image point, can be analyzed for visualization, characterization, and quantification of the disease state in biological tissue.

The overall goal of this dissertation was to investigate the potential of label-free HSI technology combined with machine learning methods as a noninvasive diagnostic tool for quantitative detection and delineation of head and neck cancer. More specifically, this dissertation work has two applications: the early detection of cancer, and surgical guidance. To achieve this, we had four different aims. The first two aims evaluated the diagnostic performance of HSI and machine learning algorithms at differentiating cancer from normal tissue in preclinical animal models, including a subcutaneous cancer model (Aim 1), and a chemically-induced tongue carcinogenesis model (Aim 2). The last two aims investigated the detection and delineation of head and neck cancer in a surgical animal model (Aim 3) and fresh surgical specimens of human patients (Aim 4).