Abstract
Research on undersampled magnetic resonance image (MRI) reconstruction can increase the speed of MRI imaging and reduce patient suffering. In this paper, an undersampled MRI reconstruction method based on Generative Adversarial Networks with the Self-Attention mechanism and the Relative Average discriminator (SARA-GAN) is proposed. In our SARA-GAN, the relative average discriminator theory is applied to make full use of the prior knowledge, in which half of the input data of the discriminator is true and half is fake. At the same time, a self-attention mechanism is incorporated into the high-layer of the generator to build long-range dependence of the image, which can overcome the problem of limited convolution kernel size. Besides, spectral normalization is employed to stabilize the training process. Compared with three widely used GAN-based MRI reconstruction methods, i.e., DAGAN, DAWGAN, and DAWGAN-GP, the proposed method can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measure(SSIM), and the details of the reconstructed image are more abundant and more realistic for further clinical scrutinization and diagnostic tasks.
Highlights
magnetic resonance image (MRI) can carry out the non-invasive examination of the internal tissues of the human body, so it is widely used in clinical pathological examination and diagnosis (Liang and Lauterbur, 2000; Kabasawa, 2012)
DAWGAN and DAWGAN-GP perform slightly better than De-Aliasing Generative Adversarial Networks (DAGAN), but there is still a big gap compared with full sampling MRI
We find that except for 10% 2D Gaussian sampling experiment we have a similar performance between DAWGAN-GP and SARA-Generative Adversarial Networks (GAN) (p = 0.1849), other experiments have demonstrated that our SARA-GAN has outperformed other methods significantly
Summary
MRI can carry out the non-invasive examination of the internal tissues of the human body, so it is widely used in clinical pathological examination and diagnosis (Liang and Lauterbur, 2000; Kabasawa, 2012). The excessive scanning time of MRI limits its clinical application, and this problem is prominent for high-resolution imaging. Compressed sensing (CS) (Lustig et al, 2008, 2010) is a conventional method for solving this problem, it uses the compressibility and sparsity of the signal to reduce k-space sampling and achieve fast imaging. The methods surrounding compressed sensing for fast MRI mainly include non-Cartesian CS (Haldar et al, 2011; Wang et al, 2012), combination parallel imaging. The above-mentioned methods based on compressed sensing have achieved good results, they all rely on the prior knowledge extracted from the image, which limits the use of the above methods to a certain extent
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