Feasibility study of using masked auto-encoder for a streak artifact reduction in sparse view CT

In this study, we investigated the feasibility of using masked auto-encoder (MAE) for sinogram inpainting to reduce a streak artifact in sparse view CT, taking their successful application in image inpainting tasks. To handle the sparse view sinogram, the flattener operator, which flattened the image patch into a token, was modified to handle the 1D image patches. We compared the artifact reduction performance for trained MAE using random sampling (i.e., MAE (Random)) and periodic sparse view sampling (i.e., MAE (Sparse)). To generate the training and validation samples, a software phantom consisting of multiple simple figures was used for the CT simulation with Siddon algorithm and filtered back-projection algorithm. To evaluate the performance of MAE for streak artifact reduction in sparse view CT, we implemented RED-CNN and compared the streak artifact reduction performance of RED-CNN and MAE. SSIM and PSNR were used to quantitatively measure the performance of MAE and RED-CNN. MAE provided better performance for streak artifact reduction in sparse view CT than conventional deep-learning-based artifact reduction technique (i.e., RED-CNN). MAE with random view sampling showed better performance than with sparse view CT images. We expect that MAE for medical imaging could be applied in other fields of medical imaging study.