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Description
Title: | Recovery of high-resolution magnetic field distribution inside the brain from limited MRI data using machine learning prior |
Author(s): | Lan, Rui |
Advisor(s): | Liang, Zhi-Pei |
Department / Program: | Electrical & Computer Eng |
Discipline: | Electrical & Computer Engr |
Degree Granting Institution: | University of Illinois at Urbana-Champaign |
Degree: | M.S. |
Genre: | Thesis |
Subject(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. |
Issue Date: | 2019-04-22 |
Type: | Text |
URI: | http://hdl.handle.net/2142/105240 |
Rights Information: | Copyright 2019 Rui Lan |
Date Available in IDEALS: | 2019-08-23 |
Date Deposited: | 2019-05 |
This item appears in the following Collection(s)
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Dissertations and Theses - Electrical and Computer Engineering
Dissertations and Theses in Electrical and Computer Engineering -
Graduate Dissertations and Theses at Illinois
Graduate Theses and Dissertations at Illinois