Deep image prior with sparsity constraint for limited-angle computed tomography reconstruction

Abstract

Computed tomography (CT) from full-angle data is of great importance to medical imaging and other areas. However, restrictions during data acquisition, such as measuring the target in a limited angular range, can lead to poorer quality results due to the increased difficulty of the image reconstruction problem. For challenging situations like this, traditional techniques may not be able to produce CT images with enough quality. In this study, we propose software for reconstruction of limited-angle CT images using a combination of deep image prior (DIP) and matching pursuit (MMP). Evaluation of our proposal was performed on the dataset provided by the Helsinki Tomographic Challenge 2022 (HTC 2022). Our results indicate that, by using MMP as a regularizer for the problem, the quality of DIP image reconstructions can be improved. Furthermore, we obtained images with a minimum Matthews correlation coefficient of 0.936 in five of seven difficulty levels of the HTC 2022, showing that it is a promising technique for this application.