Preliminary study of intensity weighted region-of-interesting image reconstruction using iterative algorithm

Sparse-view CT inspired by a compressive sensing (CS) theory, which acquires sparsely sampled data in projection angles to reconstruct volumetric images of the scanned object, is under active research for low-dose imaging. Also, region of interest (ROI) imaging method is one of the reasonable approaches to reducing the integral dose to the patient and the risk of overdose. In this study, we combined the two approaches together to achieve an ultra-low-dose imaging: a sparse-view imaging and the intensity-weighted region-of-interest (IWROI) imaging. IWROI imaging technique is particularly interesting because it can reduce the imaging radiation dose substantially to the structures away from the imaging target, while allowing a stable solution of the reconstruction problem in comparison with the interior problem. We used a total-variation (TV) minimization algorithm that exploits the sparseness of the image derivative magnitude and can reconstruct images from sparse-view data. In this study, we implemented an imaging mode that combines a sparse-view imaging and an ROI imaging. We obtained promising results and believe that the proposed scanning approach can help reduce radiation dose to the patients while preserving good quality images for applications such as image-guided radiation therapy. We are in progress of applying the method to the real data.