A wavelet gradient sparsity based algorithm for reconstruction of reduced-view tomography datasets obtained with a monochromatic synchrotron-based X-ray source.

High-resolution synchrotron computed tomography (CT) is very helpful in the diagnosis and monitor of chronic diseases including osteoporosis. Osteoporosis is characterized by low bone mass and cortical bone porosity best imaged with CT. Synchrotron CT requires a large number of angular projections to reconstruct images with high resolution for detailed and accurate diagnosis. However, this poses great risks and challenges for serial in-vivo human and animal imaging due to a large amount of X-ray radiation dose required that can damage living specimens. Also, longer scan times are associated with increased risk of specimen movement and motion artifact in the reconstructed images. We developed a wavelet-gradient sparsity based algorithm to be utilized as a synchrotron tomography reconstruction technique allowing accurate reconstruction of cortical bone porosity assessed for in-vivo preclinical study which significantly reduces the radiation dose and scan time required while maintaining satisfactory image resolution for diagnosis. The results of our study on a rat forelimb sample imaged in the Biomedical Imaging and Therapy Bending Magnet (BMIT-BM) beamline at the Canadian Light Source show that the proposed algorithm can produce satisfactory image quality with more than 50 percent X-ray dose reduction as indicated by both visual and quantitative-based performance.