TY - JOUR
T1 - Structure-adaptive CBCT reconstruction using weighted total variation and hessian penalties
AU - Shi, Qi
AU - Sun, Nanbo
AU - Sun, Tao
AU - Wang, Jing
AU - Tan, Shan
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China (NNSFC), under Grant Nos.60971112 and 61375018, and Fundamental Research Funds for the Central Universities, under Grant No. 2012QN086. J. Wang was supported in part by grants from the Cancer Prevention and Research Institute of Texas (RP130109 and RP110562-P2), the National Institute of Biomedical Imaging and Bioengineering (R01 EB020366) and a grant from the American Cancer Society (RSG-13-326-01-CCE). We would like to thank Dr. Damiana Chiavolini for editing the paper.
Publisher Copyright:
© 2016 Optical Society of America.
PY - 2016/9/1
Y1 - 2016/9/1
N2 - The exposure of normal tissues to high radiation during cone-beam CT (CBCT) imaging increases the risk of cancer and genetic defects. Statistical iterative algorithms with the total variation (TV) penalty have been widely used for low dose CBCT reconstruction, with state-of-the-art performance in suppressing noise and preserving edges. However, TV is a first-order penalty and sometimes leads to the so-called staircase effect, particularly over regions with smooth intensity transition in the reconstruction images. A second-order penalty known as the Hessian penalty was recently used to replace TV to suppress the staircase effect in CBCT reconstruction at the cost of slightly blurring object edges. In this study, we proposed a new penalty, the TV-H, which combines TV and Hessian penalties for CBCT reconstruction in a structure-adaptive way. The TV-H penalty automatically differentiates the edges, gradual transition and uniform local regions within an image using the voxel gradient, and adaptively weights TV and Hessian according to the local image structures in the reconstruction process. Our proposed penalty retains the benefits of TV, including noise suppression and edge preservation. It also maintains the structures in regions with gradual intensity transition more successfully. A majorization-minimization (MM) approach was designed to optimize the objective energy function constructed with the TV-H penalty. The MM approach employed a quadratic upper bound of the original objective function, and the original optimization problem was changed to a series of quadratic optimization problems, which could be efficiently solved using the Gauss-Seidel update strategy. We tested the reconstruction algorithm on two simulated digital phantoms and two physical phantoms. Our experiments indicated that the TV-H penalty visually and quantitatively outperformed both TV and Hessian penalties.
AB - The exposure of normal tissues to high radiation during cone-beam CT (CBCT) imaging increases the risk of cancer and genetic defects. Statistical iterative algorithms with the total variation (TV) penalty have been widely used for low dose CBCT reconstruction, with state-of-the-art performance in suppressing noise and preserving edges. However, TV is a first-order penalty and sometimes leads to the so-called staircase effect, particularly over regions with smooth intensity transition in the reconstruction images. A second-order penalty known as the Hessian penalty was recently used to replace TV to suppress the staircase effect in CBCT reconstruction at the cost of slightly blurring object edges. In this study, we proposed a new penalty, the TV-H, which combines TV and Hessian penalties for CBCT reconstruction in a structure-adaptive way. The TV-H penalty automatically differentiates the edges, gradual transition and uniform local regions within an image using the voxel gradient, and adaptively weights TV and Hessian according to the local image structures in the reconstruction process. Our proposed penalty retains the benefits of TV, including noise suppression and edge preservation. It also maintains the structures in regions with gradual intensity transition more successfully. A majorization-minimization (MM) approach was designed to optimize the objective energy function constructed with the TV-H penalty. The MM approach employed a quadratic upper bound of the original objective function, and the original optimization problem was changed to a series of quadratic optimization problems, which could be efficiently solved using the Gauss-Seidel update strategy. We tested the reconstruction algorithm on two simulated digital phantoms and two physical phantoms. Our experiments indicated that the TV-H penalty visually and quantitatively outperformed both TV and Hessian penalties.
UR - http://www.scopus.com/inward/record.url?scp=84989868797&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84989868797&partnerID=8YFLogxK
U2 - 10.1364/BOE.7.003299
DO - 10.1364/BOE.7.003299
M3 - Article
C2 - 27699100
AN - SCOPUS:84989868797
SN - 2156-7085
VL - 7
SP - 3299
EP - 3322
JO - Biomedical Optics Express
JF - Biomedical Optics Express
IS - 9
M1 - #261108
ER -