TY - GEN
T1 - Investigation on scale-based neighborhoods in MRFs for statistical iterative reconstruction
AU - Zhang, Hao
AU - Liu, Yan
AU - Wang, Jing
AU - Ma, Jianhua
AU - Han, Hao
AU - Liang, Zhengrong
PY - 2013
Y1 - 2013
N2 - Statistical iterative reconstruction (SIR) algorithms have shown advantages over the conventional filtered back-projection method for low-dose computed tomography (CT) reconstruction. For the SIR algorithms, the regularization term plays a critical role on determining the performance. One commonly used regularization is the quadratic-form Gaussian Markov random field (MRF), which penalizes differences among neighboring pixels in a small fixed window without considering discontinuities in images, thus may lead to over smoothing of edges or fine structures. In this work, we presented a quadratic-form MRF-based regularization with varying window size determined by the object scale, which is a descriptor of the image uniformity. For a uniform region (object scale is large), a larger MRF window is adopted because the coupling between the central pixel and its neighbors is strong; while for the interface region (object scale is small), a smaller MRF window is employed since the coupling is weak. The presented regularization term is incorporated into the penalized weighted least-squares (PWLS) iterative reconstruction scheme to improve low-dose CT reconstruction. Simulation results with a Shepp-Logan phantom revealed the presented regularization term is superior to the conventional Gaussian MRF in terms of noise suppression and edge preservation.
AB - Statistical iterative reconstruction (SIR) algorithms have shown advantages over the conventional filtered back-projection method for low-dose computed tomography (CT) reconstruction. For the SIR algorithms, the regularization term plays a critical role on determining the performance. One commonly used regularization is the quadratic-form Gaussian Markov random field (MRF), which penalizes differences among neighboring pixels in a small fixed window without considering discontinuities in images, thus may lead to over smoothing of edges or fine structures. In this work, we presented a quadratic-form MRF-based regularization with varying window size determined by the object scale, which is a descriptor of the image uniformity. For a uniform region (object scale is large), a larger MRF window is adopted because the coupling between the central pixel and its neighbors is strong; while for the interface region (object scale is small), a smaller MRF window is employed since the coupling is weak. The presented regularization term is incorporated into the penalized weighted least-squares (PWLS) iterative reconstruction scheme to improve low-dose CT reconstruction. Simulation results with a Shepp-Logan phantom revealed the presented regularization term is superior to the conventional Gaussian MRF in terms of noise suppression and edge preservation.
UR - http://www.scopus.com/inward/record.url?scp=84897133672&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897133672&partnerID=8YFLogxK
U2 - 10.1109/NSSMIC.2013.6829374
DO - 10.1109/NSSMIC.2013.6829374
M3 - Conference contribution
AN - SCOPUS:84897133672
SN - 9781479905348
T3 - IEEE Nuclear Science Symposium Conference Record
BT - 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2013
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2013 60th IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2013
Y2 - 27 October 2013 through 2 November 2013
ER -