A Novel Image-Based Diagnostic System for Early Assessment of Acute Renal Rejection

Ayman El-Baz, BioImaging Laboratory Bioengineering Department, University of Louisville
When Nov 13, 2015
from 03:30 PM to 04:30 PM
Where Ernst Hall, Room 310
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The seminar is free and open to the public. A reception and social time begins at 3:00 p.m., Seminar at 3:30 p.m.

Abstract: Acute rejection of renal transplant, i.e., the immunological response of the human body to a foreign kidney, is the main cause of dysfunction in renal transplant patients, surpassing acute tubular necrosis (ATN) and immune drug toxicity. In the US, approximately 17,000 renal transplants are performed annually, and given the limited number of donors, the salvage of a transplanted kidney is a critically important medical concern. At present, renal transplant dysfunction is initially evaluated using blood tests and urine sampling, e.g., plasma creatinine and creatinine clearance. However, these indices have low sensitivity, since a significant change in creatinine levels is only detectable after the loss of 60% of renal function. Biopsy remains the gold standard for the assessment of renal transplant dysfunction, yet only as the last resort because of its invasiveness, high cost, and potential morbidity. Further, a relatively small needle biopsy sample may lead to over- or under-estimation of the extent of inflammation in the entire graft. The proposed study seeks to develop and validate a new noninvasive computer-aided diagnostic (CAD) software system with the ability to make an early and accurate diagnosis of transplanted kidney status using either dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) or diffusion weighted-magnetic resonance imaging (DW-MRI).

Bio: Associate Professor and University Scholar in the Department of Bioengineering at the University of Louisville, KY. Dr. El-Baz earned his bachelor's and master degrees in Electrical Engineering in 1997 and 2001. He earned his doctoral degrees in electrical engineering from the University of Louisville in 2006. In 2009, Dr. El-Baz was named a Coulter Fellow for his contribution in the biomedical translational research. Dr El-Baz has 13 years of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has developed new techniques for the accurate identification of probability mixtures for segmenting multi-modal images, new probability models, and model-based algorithms for recognizing lung nodules and blood vessels in magnetic resonance and computer tomography imaging systems, as well as new registration techniques based on multiple second-order signal statistics, all of which have been reported at multiple international conferences and journal articles. His work related to novel image analysis techniques for autism, dyslexia, and lung cancer has earned multiple awards, including the Wallace H. Coulter Foundation Early Career Translational Research Award in Biomedical Engineering Phase I & Phase II, a Research Scholar Grant from the American Cancer Society (ACS), first place at the annual Research Louisville 2002, 2005, 2006, 2008, 2009, 2010, 2011 and 2012 meetings, and the "Best Paper Award in Medical Image Processing" from the prestigious ICGST International Conference on Graphics, Vision and Image Processing (GVIP-2005).  He has been invited to present his research on image-based techniques for early diagnosis of lung cancer at the Siemens Research Corporation and Siemens Medical Solutions. He has been the PI of five research projects that are funded by the Coulter Foundation, PI of a research project funded by the American Cancer Society, and co-PI on two R01 projects awarded by the NIH. Based on these awards, he developed a new CAD system for early diagnosis of lung cancer. PulmoCADx, Inc. (St. Louis, MO 63108, USA) has licensed this lung CAD system. He has authored or coauthored more than 300 technical articles (72 journals, 8 books, 30 book chapters, 135 refereed-conference papers, 58 abstracts published in proceedings, and 12 US patents).