POCS-Transformer for MR Image Reconstruction

Motivation: Transformers excel in medical image processing but require many parameters and training data. We mitigated this issue with the POCS-Transformer method. Goal(s): POCS-Transformer goal was to enhances MR image reconstruction along with preserving image quality with various under-sampling masks. Approach: The POCS-Transformer, built on Swin-T using FastMRI data, employed binary undersampling, POCS augmentation, data consistency penalties, and was compared to VN and POCS-CycleGAN on test data. Results: POCS-Transformer outperformed POCS-CycleGAN with superior image quality and less blurring. POCS-Transformer achieved higher mean PSNR and SSIM compared to both VN and POCS-CycleGAN in knee and brain image datasets. Impact: The POCS-Transformer improves MR image reconstruction in terms of reducing blurring even under diverse under-sampling conditions. Its impact extends to healthcare and research. New questions involve its applications in medical imaging, merging traditional and modern methods to inspire further innovations.