A Deep Learning Method to Remove Motion Artifacts in Fetal MRI

Motion artifacts are a common issue in fetal MR imaging that limit the visibility of essential fetal anatomy. In such cases, the sequence acquisition must be repeated in order for an accurate diagnosis. This study introduces a deep learning approach utilizing a Generative Adversarial Network (GAN) framework for removing motion artifacts in fetal MRIs. Results exceeded current state-of-the-art methods by achieving an average SSIM of 93.7%, and PSNR of 33.5dB. The presented network demonstrates rapid and accurate results that can be advantageous in clinical use.