TY - GEN
T1 - A multi-channel image reconstruction method for grating-based X-ray phase-contrast computed Tomography
AU - Xu, Qiaofeng
AU - Sawatzky, Alex
AU - Anastasio, Mark A.
PY - 2014
Y1 - 2014
N2 - In This work, we report on The development of an advanced multi-channel (MC) image reconstruction algorithm for grating-based X-ray phase-contrast computed Tomography (GB-XPCT). The MC reconstruction method we have developed operates by concurrently, rather Than independently as is done conventionally, reconstructing Tomographic images of The Three object properties (absorption, small-Angle scattering, refractive index). By jointly estimating The object properties by use of an appropriately defined penalized weighted least squares (PWLS) estimator, The 2nd order statistical properties of The object property sinograms, including correlations between Them, can be fully exploited To improve The variance vs. resolution Tradeoff of The reconstructed images as compared To existing methods. Channel-independent regularization strategies are proposed. To solve The MC reconstruction problem, we developed an advanced algorithm based on The proximal point algorithm and The augmented Lagrangian method. By use of experimental and computer-simulation data, we demonstrate That by exploiting inter-channel noise correlations, The MC reconstruction method can improve image quality in GB-XPCT.
AB - In This work, we report on The development of an advanced multi-channel (MC) image reconstruction algorithm for grating-based X-ray phase-contrast computed Tomography (GB-XPCT). The MC reconstruction method we have developed operates by concurrently, rather Than independently as is done conventionally, reconstructing Tomographic images of The Three object properties (absorption, small-Angle scattering, refractive index). By jointly estimating The object properties by use of an appropriately defined penalized weighted least squares (PWLS) estimator, The 2nd order statistical properties of The object property sinograms, including correlations between Them, can be fully exploited To improve The variance vs. resolution Tradeoff of The reconstructed images as compared To existing methods. Channel-independent regularization strategies are proposed. To solve The MC reconstruction problem, we developed an advanced algorithm based on The proximal point algorithm and The augmented Lagrangian method. By use of experimental and computer-simulation data, we demonstrate That by exploiting inter-channel noise correlations, The MC reconstruction method can improve image quality in GB-XPCT.
UR - https://www.scopus.com/pages/publications/84901614496
UR - https://www.scopus.com/pages/publications/84901614496#tab=citedBy
U2 - 10.1117/12.2043732
DO - 10.1117/12.2043732
M3 - Conference contribution
AN - SCOPUS:84901614496
SN - 9780819498267
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2014
PB - SPIE
T2 - Medical Imaging 2014: Physics of Medical Imaging
Y2 - 17 February 2014 through 20 February 2014
ER -