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Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy

1Schools of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA. Electronic address:
2Nuclear & Radiological Engineering and Medical Physics Programs, The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
3Center for Advanced Radiotherapy Technologies and Department of Radiation Oncology, University of California San Diego, La Jolla, CA, USA.
4Departments of Electrical & Computer and Biomedical Engineering, Boston University, Boston, MA, USA.
Elsevier Science
Publication Date:
Med Image Anal
Volume Number:
Issue Number:
Med Image Anal. 2013 Apr;17(3):387-400.
PubMed ID:
Deformable image registration, Multimodal registration, Mutual information, Shading correction, Scatter removal
Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Lou Y., Niu T., Jia X., Vela P.A., Zhu L., Tannenbaum A.R. Joint CT/CBCT Deformable Registration and CBCT Enhancement for Cancer Radiotherapy. Med Image Anal. 2013 Apr;17(3):387-400. PMID: 23433756. PMCID: PMC3640424.
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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.

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