Which GAN? A comparative study of generative adversarial network-based fast MRI reconstruction
Which GAN? A comparative study of generative adversarial network-based fast MRI reconstruction
复制标题
哪个 GAN？
DOI：
10.1098/rsta.2020.0203
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发表时间：
2021-06-28
期刊：
影响因子：
5
通讯作者：
Yang, Guang
中科院分区：
文献类型：
--
作者：
Lv, Jun;Zhu, Jin;Yang, Guang
快速磁共振成像（MRI）对于临床应用至关重要，可以减轻运动伪影并增加患者吞吐量。K空间欠采样是加速MR采集的明显方法。然而，k空间数据的欠采样可能导致重建图像的模糊和混叠伪影。最近，有几项研究提出使用基于深度学习的数据驱动模型进行MRI重建，并取得了可喜的成果。然而，这些方法的比较仍然有限，因为模型没有在相同的数据集上训练，验证策略可能不同。这项工作的目的是进行比较研究，以调查生成对抗网络（GAN）为基础的模型，用于MRI重建。我们重新实现和基准测试了四种广泛使用的基于GAN的架构，包括DAGAN，ReconGAN，RefineGAN和KIGAN。这四个框架分别使用两倍、四倍和六倍加速度在大脑、膝盖和肝脏MRI图像上进行训练和测试，并使用随机欠采样掩码。定量评估和定性可视化都表明，与其他基于GAN的方法相比，RefineGAN方法在重建方面具有上级性能，具有更好的准确性和感知质量。这篇文章是“协同断层图像重建：第1部分”主题的一部分。
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’.