Peter Kellman, Senior Scientist at National Heart Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA

Title: Next Generation Cardiac MRI

Abstract:

Cardiac MR imaging is challenging due to the need to image during cardiac and respiratory motion. Breath-held approaches lead to lengthy studies as well as image artifacts. Additionally, the analysis of the large number of images represents a workflow challenge and is often subjective. Motion correction techniques employing non-rigid image registration maybe used to acquire cardiac images during normal free-breathing thus improving the clinical workflow during scanning and improve the image quality by eliminating respiratory motion artifacts. Quantification may be used to reduce the number of raw images needed for analysis and provide objective measures for diagnosis of disease. Application of myocardial perfusion quantification for detection of ischemic heart disease will be described. A fully automatic in-line implementation is based on the open source Gadgetron streaming software image reconstruction framework. The Gadgetron framework has being extended to incorporate functionality for machine learning, and initial applications will be described.

Biography:

Dr. Peter Kellman is a senior scientist at the National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD.  He received his B.S. in Electrical Engineering in 1975 from Carnegie-Mellon University, and his M.S. (1977) and Ph.D. (1979) in Electrical Engineering from Stanford University.  He worked in the field of signal processing and analysis for National Security for over 20 years prior to joining NIH in 1999. He received the National Intelligence Distinguished Service Medal in 2000 for his contributions to the Intelligence community. He transitioned to medical signal processing at NIH. His research interests are in signal enhancement, detection and estimation, and he currently works on developing improved methods for cardiac imaging.