Motion correction combining Unet++ and swin transformer for magnetic resonance image

During magnetic resonance imaging (MRI) scanning, the patient's movement may introduce motion artifacts, leading to a decrease in image quality and potentially affecting diagnostic accuracy. To address this issue, this work proposes a new retrospective motion correction method called SUPMARNet. This method is based on Unet++, retaining its framework structure and replacing the convolutional layers of its encoder and decoder with Swin Transformer Block. Proposed model integrates the features of Unet++'s multi-layer feature fusion with Swin Transformer's capacity for capturing long-distance dependencies, thereby optimizing the utilization of distinct features from each layer and enhancing the model's capacity to recognize and eliminate motion artifacts. The experimental results demonstrate that the model can accurately recognize and remove motion artifacts while preserving image details, outperforming existing comparative methods.