TY - GEN
T1 - Preliminary Study of Image Reconstruction from Sparse-View Data in Phase-Contrast CT
AU - Zhang, Zheng
AU - Chen, Buxin
AU - Xia, Dan
AU - Sikdy, Emil Y.
AU - Anastasio, Mark
AU - Pan, Xiaochuan
N1 - This work was supported in part by NIH Grant Nos. R01-EB026282, R01-EB023968, and 1R21CA263660-01A1. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - phase contrast CT (PCCT)
KW - primal-dual algorithm
KW - sparse view
KW - total variation
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U2 - 10.1109/NSS/MIC44845.2022.10399113
DO - 10.1109/NSS/MIC44845.2022.10399113
M3 - Conference contribution
AN - SCOPUS:85185389117
T3 - 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference
BT - 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022
Y2 - 5 November 2022 through 12 November 2022
ER -