TY - JOUR
T1 - Joint CT/CBCT deformable registration and CBCT enhancement for cancer radiotherapy
AU - Lou, Yifei
AU - Niu, Tianye
AU - Jia, Xun
AU - Vela, Patricio A.
AU - Zhu, Lei
AU - Tannenbaum, Allen R.
N1 - Funding Information:
This project was supported by grants from the National Center for Research Resources (P41-RR-013218) and the National Institute of Biomedical Imaging and Bioengineering (P41-EB-015902) of the National Institutes of Health. This work was also supported by the NIH grant R01 MH82918 as well as a grant from AFOSR. This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Dr. X. Jia would like to acknowledge the support from the Early Career Award from Thrasher Research Fund. Dr. T. Niu and Dr. L. Zhu are supported by Georgia Institute of Technology new faculty startup funding and the NIH under the Grant No. 1R21EB012700-01A1. We thank NVIDIA for providing GPU cards on which this work is performed. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics .
PY - 2013/4
Y1 - 2013/4
N2 - This paper details an algorithm to simultaneously perform registration of computed tomography (CT) and cone-beam computed (CBCT) images, and image enhancement of CBCT. The algorithm employs a viscous fluid model which naturally incorporates two components: a similarity measure for registration and an intensity correction term for image enhancement. Incorporating an intensity correction term improves the registration results. Furthermore, applying the image enhancement term to CBCT imagery leads to an intensity corrected CBCT with better image quality. To achieve minimal processing time, the algorithm is implemented on a graphic processing unit (GPU) platform. The advantage of the simultaneous optimization strategy is quantitatively validated and discussed using a synthetic example. The effectiveness of the proposed algorithm is then illustrated using six patient datasets, three head-and-neck datasets and three prostate datasets.
AB - This paper details an algorithm to simultaneously perform registration of computed tomography (CT) and cone-beam computed (CBCT) images, and image enhancement of CBCT. The algorithm employs a viscous fluid model which naturally incorporates two components: a similarity measure for registration and an intensity correction term for image enhancement. Incorporating an intensity correction term improves the registration results. Furthermore, applying the image enhancement term to CBCT imagery leads to an intensity corrected CBCT with better image quality. To achieve minimal processing time, the algorithm is implemented on a graphic processing unit (GPU) platform. The advantage of the simultaneous optimization strategy is quantitatively validated and discussed using a synthetic example. The effectiveness of the proposed algorithm is then illustrated using six patient datasets, three head-and-neck datasets and three prostate datasets.
KW - Deformable image registration
KW - Multimodal registration
KW - Mutual information
KW - Scatter removal
KW - Shading correction
UR - http://www.scopus.com/inward/record.url?scp=84875272090&partnerID=8YFLogxK
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U2 - 10.1016/j.media.2013.01.005
DO - 10.1016/j.media.2013.01.005
M3 - Article
C2 - 23433756
AN - SCOPUS:84875272090
SN - 1361-8415
VL - 17
SP - 387
EP - 400
JO - Medical Image Analysis
JF - Medical Image Analysis
IS - 3
ER -