An iterative reconstruction network for incomplete projections of static CT

The static CT by Nanovision, as a new CT scanning formula, assembles a multi-source array and a ring detector array on two parallel planes with a fixed offset. The advantage of this configuration is that each source only needs to be rotated over a smaller angle range to complete a full scan than with conventional CT systems. However, the large cone angle from the source to the detector and the distribution of multiple sources lead to severe incomplete projections during the scanning process. To address this issue, this paper proposes a deep iterative network based on directional TV regularization. The network employs a tensorization module suitable for the static CT geometry in the forward and back-projection steps, and the regularization term adopts a directional TV deep learning model, which enables end-to-end reconstruction of incomplete data in the static CT. Experimental results demonstrate that the proposed method can effectively eliminate sparse artifacts, uneven artifacts and noise, and can obtain high quality images.