Deep learning-based medical image reconstruction for multi-contrast magnetic resonance imaging
type: CreativeWork
description: With the emergence of deep learning, their successful applications have been witnessed in various computer-vision tasks. As computational power has grown over the past decades, it has become possible to design deeper and more complex architectures to better extract features from the raw data, which can ultimately achieve better performance. However, medical images have different properties compared to natural images, and straightforward applications of deep networks for these tasks may not be feasible in clinical settings. Therefore, in this thesis, we propose deep learning-based frameworks that leverage additional information that is typically available in clinical conditions, with a focus on the modality of magnetic resonance imaging (MRI). Specifically, we investigate the inverse problem of MRI reconstruction, aiming to accelerate MRI acquisition by under-sampling while preserving the quality of acquired images. First, we address the issue of effectively leveraging highly correlated information across contrasts in sequentially acquired multi-contrast MR scans. We design a framework to optimise the under-sampling pattern of a target MRI contrast, by exploiting the com- plementary fully-sampled reference contrast through a novel synthesis-based information extraction strategy. Meanwhile, the reconstruction network is jointly optimised to provide guidance on the selection of sampled positions, allowing for better exploitation given limited measurements to be sampled. Second, we propose to leverage information from the original acquisition domain of MRI, known as the *k*-space, which is associated with, but different from the image domain. To allow for improved reconstruction with information in both domains, we present a dual-domain reconstruction framework with specifically designed regularisations to exploit the coupled information during optimisation. Moreover, the framework is extended for scenarios of multi-contrast MRI, with the focus on reducing the cross-con…
abstract: With the emergence of deep learning, their successful applications have been witnessed in various computer-vision tasks. As computational power has grown over the past decades, it has become possible to design deeper and more complex architectures to better extract features from the raw data, which can ultimately achieve better performance. However, medical images have different properties compared to natural images, and straightforward applications of deep networks for these tasks may not be feasible in clinical settings. Therefore, in this thesis, we propose deep learning-based frameworks that leverage additional information that is typically available in clinical conditions, with a focus on the modality of magnetic resonance imaging (MRI). Specifically, we investigate the inverse problem of MRI reconstruction, aiming to accelerate MRI acquisition by under-sampling while preserving the quality of acquired images. First, we address the issue of effectively leveraging highly correlated information across contrasts in sequentially acquired multi-contrast MR scans. We design a framework to optimise the under-sampling pattern of a target MRI contrast, by exploiting the com- plementary fully-sampled reference contrast through a novel synthesis-based information extraction strategy. Meanwhile, the reconstruction network is jointly optimised to provide guidance on the selection of sampled positions, allowing for better exploitation given limited measurements to be sampled. Second, we propose to leverage information from the original acquisition domain of MRI, known as the *k*-space, which is associated with, but different from the image domain. To allow for improved reconstruction with information in both domains, we present a dual-domain reconstruction framework with specifically designed regularisations to exploit the coupled information during optimisation. Moreover, the framework is extended for scenarios of multi-contrast MRI, with the focus on reducing the cross-con…
author: Yang, Junwei
language: eng
keywords: Deep Learning, MRI, Reconstruction
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/ca0f0b5c-7930-4a6a-aae0-6dcd5ed62b1a/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2023-12-13
date modified: 2026-07-22T22:23:57Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.110006