Recovery of high-resolution magnetic field distribution inside the brain from limited MRI data using machine learning prior
Lan, Rui
Loading…
Permalink
https://hdl.handle.net/2142/105240
Description
Title
Recovery of high-resolution magnetic field distribution inside the brain from limited MRI data using machine learning prior
Author(s)
Lan, Rui
Issue Date
2019-04-22
Director of Research (if dissertation) or Advisor (if thesis)
Liang, Zhi-Pei
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Field map
Super-resolution
Generative adversarial networks
Abstract
High-resolution field maps in brain magnetic resonance imaging (MRI) scans provide the field distribution information inside the brain which is essential in reconstructing high-quality MR images with no artifacts and distortions. These high-quality images are highly desired in clinical applications. However, the high-resolution field maps, which are used to obtain high-quality MR images, come with the cost of scan time. Recent advances in deep neural networks, particularly the generative adversarial networks (GANs), can learn the prior information through examples and generate the high-resolution field map using only one low-resolution field map counter- part. In this work, we apply the deep learning methods to solve the field map super-resolution problem and show that our GAN-based approach has the potential to generate the high-resolution field maps as a post-processing step and to speed up many clinical MRI applications.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
