Motion artifacts reduction in 4DCBCT based on motion-compensated robust principal component analysis

Conventional Cone-Beam Computed Tomography (CBCT) acquisition suffers from motion blurring problems of moving organs, especially the respiratory motion at thorax region, and consequently it may result in inaccuracy in the localizing the target of treatment and verifying delivered dose in radiation therapy. Although 4D-CBCT reconstruction technology is available to alleviate the motion blurring artifacts with the strategy of projection sorting tuned by respiratory bins, it introduces under-sampled problems. Aiming to precisely estimate the motion information of individual 4D-CBCT reconstructions, the proposed method combines the motion variable matrixes extracted from independent 4D-CBCT reconstructions using Robust Principal Component Analysis (RPCA) and the prior reconstructed image from fullsampled projections together and incorporate into iterative reconstruction framework, defining the Motion Compensated RPCA (MC-RPCA) method. Both simulation data and real data have been tested to verify the improvement in image quality at individual reconstructed phases by MC-RPCA. It can be obviously observed that the image quality the MC-RPCA method is improved with distinct features, especially in two regions of interest (ROI) with moving tissues. Quantitative evaluations indicate that large improvements in the Structural Similarity Index (SSIM) and Contrast-to-Noise Ratio(CNR) are achieved at the diaphragm slice by our method when comparing with MKB and the Prior Image Constraint Compressed Sensing (PICCS) algorithm, respectively.