Motion artifact correction in fetal MRI based on a Generative Adversarial network method

Fetal MR imaging is subject to artifacts, where the most common type is caused by motion. These artifacts can appear as blurring and/or ghosting in the affected sequences. Currently if the motion artifact is severe or covers essential fetal tissue, the sequence acquisition must be repeated for diagnostic decision-making. We propose a novel deep learning network to reduce and remove motion artifacts in fetal MRIs. It follows a Generative Adversarial Network (GAN) framework where the Generator consists of an Autoencoder structure containing Residual blocks with Squeeze and Excitation (SE), and the Discriminator uses a sequential Convolutional Neural Network (CNN) design. The loss function is composed of weighted subcomponents involving WGAN, L1, and perceptual losses. The proposed network was trained on a synthetically created motion artifact dataset, and further validated on real motion-degraded images. The creation of the synthetic dataset consisted of randomly modifying the k-space of each scan. On the synthetic dataset, the proposed network achieved an average SSIM and PSNR of 93.7 % and 33.5 dB respectively. For the real motion affected dataset, the proposed network attained an average BRISQUE score of 21.1. These results outperformed current state-of-the-art techniques including BM3D, RED-Net, NLM filtering, and WGAN-VGG. The presented network facilitates rapid and accurate post-processing for fetal MRI. It can also improve diagnostic accuracy and can save time and money by reducing the number of rescans caused by severe motion artifacts.