Application of a dual-resolution voxellation scheme to small ROI reconstruction in iterative CBCT for the reduction of computational cost

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In iterative methods for cone-beam computed tomography (CBCT) reconstruction, the use of a huge system matrix is the primary computational bottleneck and is still an obstacle to the more widespread use of these methods in practice. In this paper, to put iterative methods to practical applications, we propose a pragmatic idea, the-so-called dual-resolution voxellation scheme, for a small region-of-interest (ROI) reconstruction in CBCT in which voxels outside the ROI are binned with a double resolution such as 2×2×2, 4×4×4, 8×8×8, 16×16×16, etc., and the voxel sizewithin the ROI remains unchanged. In some situations of medical diagnosis, physicians are interested only in a small ROI containing a target diagnosis from the examined structure. We implemented an efficient compressed-sensing (CS)-based reconstruction algorithm with the proposed voxellation scheme incorporated and performed both simulation and experimental works to investigate the imaging characteristics. Our results indicate that the proposed voxellation scheme seems to be effective in reducing the computational cost considerably for a small ROI reconstruction in iterative CBCT, with the image quality inside the ROI not being noticeably impaired.