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New CPRIT Grant to Develop Smart Surgical Microscope and Artificial Intelligence

Dr. Baowei Fei, the Cecil H. and Ida Green Chair in Systems Biology Science at UT Dallas, is developing a smart surgical microscope that uses hyperspectral imaging and artificial intelligence to detect cancer cells during surgery. He recently received a $1.6 million grant from the Cancer Prevention & Research Institute of Texas (CPRIT) to further develop the technology. Hyperspectral imaging, originally used in satellite imagery, orbiting telescopes and other applications, goes beyond what the human eye can see as cells are examined under ultraviolet and near-infrared lights at micrometer resolution. By analyzing how cells reflect and absorb light across the electromagnetic spectrum, experts can get a spectral image of cells that is as unique as a fingerprint. For more information, visit the following website:
https://www.utdallas.edu/news/research/cancer-smart-surgical-microscope-fei-2019/

New NIH Grant to Support Research on Deep Learning for CT Image Standardization

Lung cancer is the leading cause of cancer death and one of the most common cancers among both men and women in the United States. Recent advances in high-resolution imaging set the stage for radiomics to become an active emerging field in cancer research. However, the promise of radiomics is limited by a lack of image standardization tools, because computed tomography (CT) images are often acquired using scanners from different vendors with customized acquisition parameters, posing a fundamental challenge to radiomic studies across sites. To overcome this challenge, especially for large-scale, multi-site radiomic studies, advanced algorithms are required to integrate, standardize, and normalize CT images from multiple sources. We propose to develop STAN-CT, a deep learning software package that can automatically standardize and normalize a large volume of diagnostic images to facilitate cross-site large-scale image feature extraction for lung cancer characterization and stratification. STAN-CT will enable a wide range of radiomic researches to identify diagnostic image features that strongly associated with lung cancer prognosis.

Dr. Baowei Fei Honored at the University Investiture Ceremony

Dr. Baowei Fei is honored at the University Investiture Ceremony. President Richard C. Benson and Dr. Poras Balsara, interim dean of the Erik Jonsson School of Engineering and Computer Science, presented a medallion to Dr. Baowei Fei, Cecil H. and Ida M. Green Chair in Systems Biology Science. Twelve faculty members who were formally honored hold professorships and chairs in the Office of Graduate Education, School of Behavioral and Brain Sciences, Erik Jonsson School of Engineering and Computer Science, Naveen Jindal School of Management and the School of Natural Sciences and Mathematics. Dr. Baowei Fei, an imaging scientist and cancer scholar whose work has transformed medical imaging and intervention for cancer care and who teaches in the Department of Bioengineering in the Jonsson School, is among those recognized at the ceremony.
https://www.utdallas.edu/news/faculty/investiture-ceremony-to-honor-faculty-donors/

NBC News Featured Research Work on Molecular Imaging and Targeted Biopsy

NBC News featured the research work by Dr. Baowei Fei and his team. The technology that was developed in his research lab is called molecular imaging directed, 3D ultrasound-guided biopsy, which improved upon 2D systems by allowing for earlier detection of potentially cancerous cells in the prostate. The new technology has been evaluated by an early phase clinical trial and the clinical finds were published Journal of Urology (Fei et al, Feasibility and Initial Results: Fluciclovine Positron Emission Tomography/Ultrasound Fusion Targeted Biopsy of Recurrent Prostate Cancer. Journal of Urology. 2019;202(2):413-421). For more information, read the PDF file of the clinical paper or visit the following website for the NBC news.
https://www.nbcdfw.com/news/local/researcher-finds-cancer-earlier-with-new-imaging-technology/9491/