Incomplete-scan CT reconstruction via deep learning: a review

Computed tomography (CT) is a significant clinical detection method without invasive procedures. However, high-dose radiation is associated with elevated risk of cancer. Low-dose CT reconstruction techniques have the potential to significantly reduce this risk. These techniques reduce radiation dose by two means: low radiation intensity and incomplete scans. The focus of this review is incomplete-scan CT reconstruction techniques, including sparse-view and limited-angle CT reconstruction. In the context of incomplete scans, CT images obtained by conventional CT reconstruction techniques exhibit noises and streak artifacts. Deep learning techniques are changing this situation. However, there are few reviews that recently focus on the fast developing area. This paper reviews recent research on CT reconstruction of incomplete scans via deep learning and classifies them into two categories including image domain and reconstruction domain processing methods. This study also evaluates the strengths and limitations of these approaches and discusses future prospects, highlighting promising technologies and directions.