Amir Amini

Professor

Amir Amini, Ph.D., is Professor and Endowed Chair in Bioimaging in the ECE Dept. at the University of Louisville. He received B.S. in Electrical Engineering from the University of Massachusetts, Amherst with high honors when at 18 he was the youngest graduate of the University and Ph.D. in Electrical Engineering and Computer Science from University of Michigan Artificial Intelligence Laboratory. After postdoctoral work on biomedical imaging, he was on faculty at Yale as Assistant Professor. He then moved to Washington University in St. Louis where he was Assistant Professor and then Associate Professor with tenure. He has been at University of Louisville since August 2006 where he directs the Medical Imaging Laboratory and conducts research in the area of computational imaging, cardiovascular imaging, and on medical image analysis including deep learning.  Dr. Amini has been on the editorial board of IEEE Trans. On Medical Imaging since 1999 , Elsevier's journal of Computerized Medical Imaging and Graphics since 2012,  IEEE Trans. on Biomedical Engineering since 2014, IEEE Journal of Biomedical Health Informatics (2016-2019), the IEEE Open Journal of Engineering in Medicine and Biology since 2019, and IEEE Reviews in Biomedical Engineering since 2020. He has been on the scientific advisory board of IEEE Journal of Biomedical Health Informatics since 2020. Dr. Amini was elevated to grade of Fellow of the IEEE (Engineering in Medicine and Biology Society) in 2007  with the citation, ``for contributions to Cardiovascular Imaging and Medical Image Analysis.'' With more than 430,000 members in over 150 countries, IEEE (Institute of Electrical and Electronics Engineers) is the world's largest professional society dedicated to the advancement of technology. Only 0.1 percent of the active membership worldwide can be elevated to the rank of Fellow in any given year. More recently, in January 2013, he received the Distinguished Lecturer Award from the IEEE Engineering in Medicine and Biology Society. Dr. Amini served on the IEEE EMBS Administrative Committee for the term 2016-2018. He co-chaired the IEEE International Symposium on Biomedical Imaging which took place in Washington, D.C. in April 2018 and was elected to the College of Fellows of the American Institute of Medical and Biological Engineering  in 2017. He was elevated to Fellow of SPIE - the International Society for Optics, Photonics, and Imaging in 2019. Dr. Amini serves as Vice President for Publications for the IEEE Engineering in Medicine and Biology Society for the term 2020-2021.  His research has been funded by the National Science Foundation, the National Institutes of Health, private foundations, and industry.

Education

  • Postdoctoral in Imaging Science and Medical Image Computing, Yale University, 1991
  • Ph.D. in Electrical Engineering and Computer Science, University of Michigan, 1990
  • M.S.E. in Electrical Engineering, University of Michigan, 1984
  • B.S. in Electrical Engineering, University of Massachusetts , 1983

Publications

In-Vitro Validation of Flow Measurement with Phase Contrast MRI at 3T Using SPIV and SPIV-based CFD- 2014

Purpose To validate conventional phase-contrast MRI (PC-MRI) measurements of steady and pulsatile flows through stenotic phantoms with various degrees of narrowing at Reynolds numbers mimicking flows in the human iliac artery using stereoscopic particle image velocimetry (SPIV) as gold standard. Materials and Methods A series of detailed experiments are reported for validation of MR measurements of steady and pulsatile flows with SPIV and CFD on three different stenotic models with 50%, 74%, and 87% area occlusions at three sites: two diameters proximal to the stenosis, at the throat, and two diameters distal to the stenosis. Results Agreement between conventional spin-warp PC-MRI with Cartesian read-out and SPIV was demonstrated for both steady and pulsatile flows with mean Reynolds numbers of 130, 160, and 190 at the inlet by evaluating the linear regression between the two methods. The analysis revealed a correlation coefficient of > 0.99 and > 0.96 for steady and pulsatile flows, respectively. Additionally, it was found that the most accurate measures of flow by the sequence were at the throat of the stenosis (error < 5% for both steady and pulsatile mean flows). The flow rate error distal to the stenosis was primarily found to be a function of narrowing severity including dependence on proper Venc selection. Conclusion SPIV and CFD provide excellent approaches to in vitro validation of new or existing PC-MRI flow measurement techniques.

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