Preliminary Study of Image Reconstruction from Sparse-View Data in Phase-Contrast CT

Phase-contrast computed tomography (PCCT) has gained popularity rapidly in preclinical and clinical imaging applications. PCCT often requires a large number of projections that results in considerably long scan time and high radiation dose. One approach to lower the scan time/dose in PCCT imaging is to reduce the total number of projections. However, image quality may be degraded when conventional, analytic-based algorithms are used for reconstructing images from such sparse-view PCCT data. In this work, we tailor and develop a primal-dual algorithm for PCCT image reconstruction through solving an optimization problem containing an image total variation (TV) constraint, and reconstruct image from sparse-view (30~180 views) data collected in propagation-based (PB) PCCT. Result shows that the TV algorithm generally outperforms the analytic-based algorithm, such as FBP, in terms of artifacts reduction for sparse-view image reconstruction. The work suggests that the TV algorithm may be used for PCCT image reconstruction from data containing reduced projections, which may lower the scan time and imaging dose while yielding images of practical utility.