Sparse angle CT reconstruction based on neural radial field

To address the issues of extensive artifacts and noise in sparse-angle computed tomography (CT) reconstruction, we propose a neural radial field-based sparse-angle CT reconstruction algorithm. Specifically, we utilize both newly synthesized projection views and original projection views as inputs to increase the projection views, thereby enhancing the quality of the reconstruction results. However, the newly synthesized projection images exhibit a certain degree of blur. To address this issue, we incorporate a deblurring module into the network to optimize sampling during the process, thereby achieving deblurring effects. This approach overcomes the challenges of sparse-angle CT reconstruction. We demonstrate CT reconstruction with fewer projection data (20 projections) and validate the effectiveness of our algorithm on a publicly available clinical dataset. Experimental results show that the proposed algorithm can improve the quality of CT image reconstruction, providing a feasible and effective method.