This item is under embargo and not available online per the author's request. For access information, please visit http://libanswers.wustl.edu/faq/5640.

Date of Award

Spring 5-2020

Author's School

McKelvey School of Engineering

Author's Department

Computer Science & Engineering

Degree Name

Master of Science (MS)

Degree Type

Thesis

Abstract

Image reconstruction problem, especially Magnetic Resonance(MR) Imaging reconstruction, plays an important role in real life and have a great development recently. While state-of-the-art deep learning- based image reconstruction algorithms increasingly rely on machine learning, such methods usually require ground-truth and ignore physical models, which may not be satisfied in some cases and reasonable respectively.

In this master research, we introduce our visions toward practical learning-based methods and two algorithms based on the visions, for solving realistic MR image reconstruction problems: 1) an deep learning approach by directly learning artifact-free 4D motion-resolved MR images from multiple noisy MR data plagued by streaking artifacts, without the need of ground truth, and 2) a new plug-and-play (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning imaging priors. The prior is specified through a convolutional neural network (CNN) trained to remove under-sampling artifacts from single MR data without any artifact-free ground truth.

Language

English (en)

Chair

Dr. Ulugbek Kamilov

Committee Members

Dr. Ulugbek Kamilov Dr. Hongyu An Dr. Chien-Ju Ho

Available for download on Thursday, September 05, 2047

Included in

Engineering Commons

Share

COinS