TY - GEN
T1 - Generalized least-squares CT reconstruction with detector blur and correlated noise models
AU - Stayman, J. Webster
AU - Zbijewski, Wojciech
AU - Tilley, Steven
AU - Siewerdsen, Jeffrey
PY - 2014
Y1 - 2014
N2 - The success and improved dose utilization of statistical reconstruction methods arises, in part, from their ability to incorporate sophisticated models of the physics of the measurement process and noise. Despite the great promise of statistical methods, typical measurement models ignore blurring effects, and nearly all current approaches make the presumption of independent measurements - disregarding noise correlations and a potential avenue for improved image quality. In some imaging systems, such as flat-panel-based cone-beam CT, such correlations and blurs can be a dominant factor in limiting the maximum achievable spatial resolution and noise performance. In this work, we propose a novel regularized generalized least-squares reconstruction method that includes models for both system blur and correlated noise in the projection data. We demonstrate, in simulation studies, that this approach can break through the traditional spatial resolution limits of methods that do not model these physical effects. Moreover, in comparison to other approaches that attempt deblurring without a correlation model, superior noise-resolution trade-offs can be found with the proposed approach.
AB - The success and improved dose utilization of statistical reconstruction methods arises, in part, from their ability to incorporate sophisticated models of the physics of the measurement process and noise. Despite the great promise of statistical methods, typical measurement models ignore blurring effects, and nearly all current approaches make the presumption of independent measurements - disregarding noise correlations and a potential avenue for improved image quality. In some imaging systems, such as flat-panel-based cone-beam CT, such correlations and blurs can be a dominant factor in limiting the maximum achievable spatial resolution and noise performance. In this work, we propose a novel regularized generalized least-squares reconstruction method that includes models for both system blur and correlated noise in the projection data. We demonstrate, in simulation studies, that this approach can break through the traditional spatial resolution limits of methods that do not model these physical effects. Moreover, in comparison to other approaches that attempt deblurring without a correlation model, superior noise-resolution trade-offs can be found with the proposed approach.
KW - Sinogram restoration
KW - correlated noise
KW - crosstalk
KW - high-resolution cone-beam computed tomography
KW - model-based iterative reconstruction
KW - projection deblurring
UR - http://www.scopus.com/inward/record.url?scp=84901610527&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84901610527&partnerID=8YFLogxK
U2 - 10.1117/12.2043067
DO - 10.1117/12.2043067
M3 - Conference contribution
AN - SCOPUS:84901610527
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 -