Journal
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
Volume 379, Issue 2200, Pages -Publisher
ROYAL SOC
DOI: 10.1098/rsta.2020.0203
Keywords
magnetic resonance imaging; generative adversarial network; reconstruction; deep learning
Categories
Funding
- National Natural Science Foundation of China [61902338]
- British Heart Foundation [PG/16/78/32402]
- Hangzhou Economic and Technologica Development Area Strategical Grant (Imperial Institute of Advanced Technology)
- European Research Council [H2020-JTI-IMI2 101005122]
- AI for Health Imaging Award 'CHAIMELEON: Accelerating the Lab to Market Transition of AI Tools for Cancer Management' [H2020-SC1-FA-DTS-2019-1 952172]
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This study investigates the use of generative adversarial network (GAN)-based models for MRI reconstruction, with RefineGAN showing superior performance in accuracy and perceptual quality compared to other GAN-based methods.
Fast magnetic resonance imaging (MRI) is crucial for clinical applications that can alleviate motion artefacts and increase patient throughput. K-space undersampling is an obvious approach to accelerate MR acquisition. However, undersampling of k-space data can result in blurring and aliasing artefacts for the reconstructed images. Recently, several studies have been proposed to use deep learning-based data-driven models for MRI reconstruction and have obtained promising results. However, the comparison of these methods remains limited because the models have not been trained on the same datasets and the validation strategies may be different. The purpose of this work is to conduct a comparative study to investigate the generative adversarial network (GAN)-based models for MRI reconstruction. We reimplemented and benchmarked four widely used GAN-based architectures including DAGAN, ReconGAN, RefineGAN and KIGAN. These four frameworks were trained and tested on brain, knee and liver MRI images using twofold, fourfold and sixfold accelerations, respectively, with a random undersampling mask. Both quantitative evaluations and qualitative visualization have shown that the RefineGAN method has achieved superior performance in reconstruction with better accuracy and perceptual quality compared to other GAN-based methods. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.
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