A sparsity-based iterative algorithm for reconstruction of micro-CT images from highly undersampled projection datasets obtained with a synchrotron X-ray source

SynchrotronX-ray MicroComputed Tomography (Micro-CT) is animaging technique which is increasingly used for non-invasivein vivo preclinicalimaging. However, it often requires a large number of projections from many different angles toreconstruct high-qualityimages leading to significantly highradiationdoses and long scan times. To utilize thisimaging technique further forin vivoimaging, we need to designreconstruction algorithms that reduce theradiationdose and scan time without reduction ofreconstructed image quality. This research is focused on using a combination of gradient-based Douglas-Rachford splitting and discretewavelet packet shrinkageimage denoising methods to design an algorithm forreconstruction of large-scale reduced-view synchrotron Micro-CTimages with acceptable quality metrics. These quality metrics are computed by comparing thereconstructed images with a high-dose referenceimagereconstructed from 1800 equally spaced projections spanning 180 °. Visual and quantitative-based performance assessment of a synthetic head phantom and a femoral cortical bone sampleimaged in the biomedicalimaging and therapy bending magnet beamline at the Canadian Light Source demonstrates that the proposed algorithm is superior to the existingreconstruction algorithms. Using the proposedreconstruction algorithm to reduce the number of projections in synchrotron Micro-CT is an effective way to reduce the overallradiationdose and scan time which improvesin vivoimaging protocols.

Source: Review of Scientific Instruments - December 8, 2016 Category: Physics Authors: S. Ali Melli, Khan A. Wahid, Paul Babyn, David M. L. Cooper and Varun P. Gopi Source Type: research