Speaker: Dr. Bo Zhu
Affiliation: Harvard Medical School / Harvard University
Abstract: Over the past few decades, top-down expert engineering has driven the creative design of tomographic imaging acquisition and reconstruction processes. Image reconstruction is challenging because analytic knowledge of the exact inverse transform may not exist a priori, especially in the presence of sensor non-idealities and noise. Thus, the standard reconstruction approach involves approximating the inverse function with multiple ad hoc stages in a signal processing chain whose composition depends on the details of each acquisition strategy. We present here a unified framework for image reconstruction, AUtomated TransfOrm by Manifold APproximation (AUTOMAP), which recasts image reconstruction as a data-driven, supervised learning task that allows a mapping between sensor and image domain to emerge from an appropriate corpus of training data. Implemented with a deep neural network, AUTOMAP is remarkably flexible in learning reconstruction transforms for a variety of acquisition strategies, utilizing a single network architecture and hyperparameters. We further demonstrate its efficiency in sparsely representing transforms along low-dimensional manifolds, resulting in superior immunity to noise and a reduction in reconstruction artifacts compared with conventional handcrafted reconstruction methods. In this talk we also describe work deploying machine learning for MR image acquisition with AUTOmated pulse SEQuence generation (AUTOSEQ), using both model-based and model-free reinforcement learning approaches to produce canonical (gradient echo) as well as non-intuitive pulse sequences that can perform spatial encoding and slice selection in unknown inhomogeneous B0 fields.
Biography: Dr. Bo Zhu is a research fellow in Radiology at Harvard Medical School in the A.A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital and a research scholar in Physics at Harvard University. His research focus investigates the application of artificial intelligence and development of machine learning techniques to accelerate high-fidelity image acquisition and reconstruction for modalities across the physical and life sciences. Dr. Zhu received his S.B. and M.Eng. in Electrical Engineering from MIT, and his Ph.D. in Biomedical Engineering at MIT at the Harvard-MIT Division of Health Sciences and Technology.
Date(s) - Feb 24, 2020
11:00 am - 12:30 pm
EE-IV Shannon Room #54-134
420 Westwood Plaza - 5th Flr., Los Angeles CA 90095