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’.

快速磁共振成像 (MRI) 对于临床应用至关重要，它可以减少运动伪影并提高患者吞吐量。 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会导致重建图像模糊和混叠伪像。最近，一些研究提出使用基于深度学习的数据驱动模型进行 MRI 重建，并取得了有希望的结果。然而，这些方法的比较仍然有限，因为模型尚未在相同的数据集上进行训练，并且验证策略可能不同。这项工作的目的是进行比较研究，以研究基于生成对抗网络 (GAN) 的 MRI 重建模型。我们重新实现并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、四倍和六倍加速度以及随机欠采样掩模在大脑、膝盖和肝脏 MRI 图像上进行了训练和测试。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 方法在重建方面取得了优异的性能，具有更好的准确性和感知质量。

本文是主题“协同断层扫描图像重建：第 1 部分”的一部分。