TY - JOUR
T1 - Vasculature-Driven Biomechanical Deformable Image Registration of Longitudinal Liver Cholangiocarcinoma Computed Tomographic Scans
AU - Cazoulat, Guillaume
AU - Elganainy, Dalia
AU - Anderson, Brian M.
AU - Zaid, Mohamed
AU - Park, Peter C.
AU - Koay, Eugene J.
AU - Brock, Kristy K.
N1 - Funding Information:
Sources of support: Dr Cazoulat reports grants from RaySearch Laboratories AB and University of Texas MD Anderson Cancer Center Co-Development and Collaboration Agreement, grants from National Cancer Institute, during the conduct of the study. Dr Brock reports grants from RaySearch Laboratories AB and University of Texas MD Anderson Cancer Center Co-Development and Collaboration Agreement, grants from National Cancer Institute, grants from Helen Black Foundation, during the conduct of the study. Dr Koay reports grants from National Cancer Institute, grants from Stand Up To Cancer, grants from Project Purple, grants from Pancreatic Cancer Action Network, from null, during the conduct of the study; other from Taylor and Francis, LLC, grants from Philips Health care, outside the submitted work. Research reported in this publication was supported in part by RaySearch Laboratories AB and University of Texas MD Anderson Cancer Center through a Co-Development and Collaboration Agreement. Research reported in this publication was supported in part by the Helen Black Image Guided Fund. Research reported in this publication was supported in part by the National Cancer Institute of the National Institutes of Health under award number 1R01CA221971-01A1 awarded to Kristy K. Brock. Research reported in this publication was supported in part by resources of the Image Guided Cancer Therapy Research Program from The University of Texas MD Anderson Cancer Center. Research reported in this publication was supported in part by The University of Texas MD Anderson Cancer Center. Dr Eugene Koay was supported by the Andrew Sabin Family Fellowship, Sheikh Ahmed Center for Pancreatic Cancer Research, Khalifa Foundation, equipment support by GE Healthcare and the Center of Advanced Biomedical Imaging, and NIH (U54CA210181.01, U54CA143837 and U01CA196403). The work was also supported by the Cancer Center Support Grant (CA016672) to MD Anderson. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
© 2019 The Authors
PY - 2020/3/1
Y1 - 2020/3/1
N2 - Purpose: Deformable image registration (DIR) of longitudinal liver cancer computed tomographic (CT) images can be challenging owing to anatomic changes caused by radiation therapy (RT) or disease progression. We propose a workflow for the DIR of longitudinal contrast-enhanced CT scans of liver cancer based on a biomechanical model of the liver driven by boundary conditions on the liver surface and centerline of an autosegmentation of the vasculature. Methods and Materials: Pre- and post-RT CT scans acquired with a median gap of 112 (32-217) days for 28 patients who underwent RT for intrahepatic cholangiocarcinoma were retrospectively analyzed. For each patient, 5 corresponding anatomic landmarks in pre- and post-RT scans were identified in the liver by a clinical expert for evaluation of the accuracy of different DIR strategies. The first strategy corresponded to the use of a biomechanical model-based DIR method with boundary conditions specified on the liver surface (BM_DIR). The second strategy corresponded to the use of an expansion of BM_DIR consisting of the auto-segmentation of the liver vasculature to determine additional boundary conditions in the biomechanical model (BM_DIR_VBC). The 2 strategies were also compared with an intensity-based DIR strategy using a Demons algorithms. Results: The group mean target registration errors were 12.4 ± 7.5, 7.7 ± 3.7 and 4.4 ± 2.5 mm, for the Demons, BM_DIR and BM_DIR_VBC, respectively. Conclusions: In regard to the large and complex deformation observed in this study and the achieved accuracy of 4.4 mm, the proposed BM_DIR_VBC method might reveal itself as a valuable tool in future studies on the relationship between delivered dose and treatment outcome.
AB - Purpose: Deformable image registration (DIR) of longitudinal liver cancer computed tomographic (CT) images can be challenging owing to anatomic changes caused by radiation therapy (RT) or disease progression. We propose a workflow for the DIR of longitudinal contrast-enhanced CT scans of liver cancer based on a biomechanical model of the liver driven by boundary conditions on the liver surface and centerline of an autosegmentation of the vasculature. Methods and Materials: Pre- and post-RT CT scans acquired with a median gap of 112 (32-217) days for 28 patients who underwent RT for intrahepatic cholangiocarcinoma were retrospectively analyzed. For each patient, 5 corresponding anatomic landmarks in pre- and post-RT scans were identified in the liver by a clinical expert for evaluation of the accuracy of different DIR strategies. The first strategy corresponded to the use of a biomechanical model-based DIR method with boundary conditions specified on the liver surface (BM_DIR). The second strategy corresponded to the use of an expansion of BM_DIR consisting of the auto-segmentation of the liver vasculature to determine additional boundary conditions in the biomechanical model (BM_DIR_VBC). The 2 strategies were also compared with an intensity-based DIR strategy using a Demons algorithms. Results: The group mean target registration errors were 12.4 ± 7.5, 7.7 ± 3.7 and 4.4 ± 2.5 mm, for the Demons, BM_DIR and BM_DIR_VBC, respectively. Conclusions: In regard to the large and complex deformation observed in this study and the achieved accuracy of 4.4 mm, the proposed BM_DIR_VBC method might reveal itself as a valuable tool in future studies on the relationship between delivered dose and treatment outcome.
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U2 - 10.1016/j.adro.2019.10.002
DO - 10.1016/j.adro.2019.10.002
M3 - Article
AN - SCOPUS:85076525365
SN - 2452-1094
VL - 5
SP - 269
EP - 278
JO - Advances in Radiation Oncology
JF - Advances in Radiation Oncology
IS - 2
ER -