Keywords: Image Reconstruction, Brain, 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.
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