Four dimensional cone-beam computed tomography reconstruction using motion tracking induced regional spatiotemporal sparsity

Four dimensional cone-beam computed tomography (4D-CBCT) has great clinical value because its ability to describe tumor and organ motion. But the challenge in 4D-CBCT reconstruction is the limited number of projections at each phase, which resulting in the reconstruction full of noise and streak artifacts with the conventional FDK algorithm. To address the problem, in this work, we propose a novel framework to reconstruct 4D-CBCT from under-sampled measurements - Motion Tracking induced Regional Spatiotemporal Sparsity (MT-RSS). In this algorithm, we try to divide the CBCT images into regions, track the regions with estimated motion field vectors through time (phase), and then apply regional spatiotemporal sparsity on the tracked regions. Subsequently, we construct a cost function for the reconstruction pass. XCAT phantom based simulation and real patient data were used to evaluate the proposed algorithm. Results show that the MT-RSS algorithm provides improved 4D-CBCT image quality with the introduction of phase-correlated information.