MEDICAL IMAGING SEMINAR

Quantitative myocardial perfusion PET parametric imaging at the voxel-level  

Dr. Hassan Mohy‐ud‐Din

Abstract: My talk will focus on robust estimation of physiological parameters in cardiac PET (Positron Emission Tomography) imaging. Robust estimation requires substantial post‐smoothing of noisy data, degrading valuable functional information of physiological and pathological importance. I will present a feasible  and  robust  approach,  called  Physiological  Clustering, to generate parametric images at the voxel‐level  that  substantially  reduces noise without  significant  loss of  spatial resolution. The proposed approach borrows tools from image processing and machine learning. The efficacy of physiological clustering is demonstrated with extensive simulations and clinical studies.

Biography: Hassan Mohy‐ud‐Din is a Clinical Research Scientist at SKMCH&RC. He completed his PhD and MSE in Electrical and Computer Engineering (ECE) and MA in Applied Mathematics and Statistics (AMS) from Johns Hopkins University (JHU) on ECE‐JHU fellowship (2009 – 2015). From 2015 – 2017 he was a postdoctoral associate in the Department of Radiology and Biomedical Imaging at Yale School of Medicine. He completed his BS in Electronics Engineering from GIK Institute of Engineering Sciences and Technology in Pakistan (2002 – 2006). His research lies at the intersection of Applied Mathematics and Medical Imaging with focus on (brain/cardiac/whole‐body) PET/CT, PET/MR, SPECT/CT and Low‐dose CT. His work on dynamic cardiac PET imaging won the 2014 SNMMI Bradley‐Alavi fellowship and the 2014 SIAM student award. He has published his work in leading scientific journals and presented it at various conferences and universities around the world. He also carries a university teaching experience of over 10 years (UET Lahore, SASSE, LUMS and John Hopkins University, USA).

Webpage – https://sites.google.com/view/hmohyud/

Date: December 7, 2017 (Thursday)

Time: 12:15 pm – 02:00 pm

Venue: Auditorium I, UOL (Defence Road)

Register online here

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