SART-Res-UNet: Fan Beam CT Image Reconstruction from Limited Projections using attention-enabled residual U-Net

CT scans significantly improve analytical competencies but uses X-Rays which will produce ionizing radiation that bring higher radiation to the living tissues. Thus, optimization of CT radiation dose has a significant concern to lower the health risks. Many manufacturers have done a greater contribution by developing technologies to reduce dosage by maintaining image quality by adding noise reduction filters, automatic exposure control, and using many iterative reconstruction algorithms. Image reconstruction algorithms play a vital role in maintaining or improving image quality in reduced-dose CT. The present research work combines the state-of-the-art reconstruction technique Simultaneous Algebraic Reconstruction Technique (SART) with a Residual U-Net network to generate images from limited number of sinograms. The proposed model is trained using sinograms corresponding head and neck and head CT images of 10 patients. The proposed model predicted superior diagnostic quality images with max PSNR of 70.23 and Structural Similarity Index Measure (SSIM) of 0.99. Thus the proposed model, SART-Res-Unet, ensures a very low radiation exposure to a patient during the repeated CT imaging sequence, which is an inevitable part of radiotherapy.