@article{Lv:2021:10.1098/rsta.2020.0203,
author = {Lv, J and Zhu, J and Yang, G},
doi = {10.1098/rsta.2020.0203},
journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
pages = {1--17},
title = {Which GAN? A comparative study of generative adversarial network (GAN) based fast MRI reconstruction},
url = {http://dx.doi.org/10.1098/rsta.2020.0203},
volume = {379},
year = {2021}
}

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TY  - JOUR
AB  - 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 bedifferent. 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 2, 4 and 6- fold accelerations with a random undersampling mask. Both quantitative evaluations and qualitative visualisation have shown that the RefineGAN method has achieved superior performance in reconstruction with better accuracy and perceptual quality compared to other GAN based methods.
AU  - Lv,J
AU  - Zhu,J
AU  - Yang,G
DO  - 10.1098/rsta.2020.0203
EP  - 17
PY  - 2021///
SN  - 1364-503X
SP  - 1
TI  - Which GAN? A comparative study of generative adversarial network (GAN) based fast MRI reconstruction
T2  - Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
UR  - http://dx.doi.org/10.1098/rsta.2020.0203
UR  - https://royalsocietypublishing.org/doi/10.1098/rsta.2020.0203
UR  - http://hdl.handle.net/10044/1/84858
VL  - 379
ER  - 

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