Time: May 26th, 13:00 pm
Location: CBIR meeting room 104
Speaker: Bo Zhao, Ph.D
Host: Rui Li
Magnetic Resonance (MR) Fingerprinting is a recent breakthrough in quantitative MR imaging. It provides an ultrafast speed to simultaneously acquire multiple tissue MR parameters (e.g., spin-spin relaxation, spin-lattice relaxation, and spin density). MR Fingerprinting features with a matched filter based approach for image reconstruction. Although this approach is simple and computationally efficient, it is heuristic in nature. In this talk, I will present our recent research on the development of a principled statistical imaging framework for MR fingerprinting. More specifically, we have formulated the image reconstruction problem as a maximum likelihood estimation problem, taking into account the Bloch equation based spin physics model and noise statistics. We have further developed a novel computational algorithm to solve the resulting nonlinear, non-convex optimization problem. We have shown analytically that with a gridding reconstruction as an initialization, the first iteration of the proposed algorithm exactly produces the conventional approach; with additional iterations, the solution can be pushed towards a statistical optimum that has a number of desired properties. I will illustrate the properties and performance of the proposed approach with simulation and in vivo experimental results.
Bo Zhao obtained the Ph.D. from the University of Illinois at Urbana-Champaign in 2014, advised by Prof. Zhi-Pei Liang. He is now a postdoctoral research fellow in the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital and Harvard Medical School, working with Prof. Lawrence Wald and Prof. Kawin Setsompop. His research is mainly focused on image formation and computational imaging (signal/image modeling, algorithms, and performance characterization), and their applications to MRI. He has received Magna Cum Laude Merit Awards from the International Society of Magnetic Resonance in Medicine (ISMRM), and coauthored two best paper awards from the Institute of Electrical and Electronic Engineering (IEEE).