期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 36, 期 1, 页码 111-123出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2016.2597313
关键词
Tumor growth prediction; image-based model personalization; nonlinear solid mechanics; elastic-growth decomposition; FDM-FEM coupling
类别
资金
- Intramural Research Program of the National Institutes of Health Clinical Center
Pancreatic neuroendocrine tumors are abnormal growths of hormone-producing cells in the pancreas. Unlike the brain which is protected by the skull, the pancreas can be significantly deformed by its surrounding organs. Consequently, the tumor shape differences observable from images at different time points arise from both tumor growth and pancreatic motion, and tumor growth model personalization may be compromised if such motion is ignored. Therefore, we incorporate pancreatic motion information derived from deformable image registration in model personalization. For more accurate mechanical interactions between tumor growth and pancreatic motion, elastic-growth decomposition is used with a hyperelastic constitutive law to model the mass effect, which allows growth modeling while conserving the mechanical properties. Furthermore, a way of coupling the finite difference method and the finite element method is proposed to greatly reduce the computation time. With both 2-[F-18]-fluoro-2-deoxy-D-glucose positron emission tomographic and contrast-enhanced computed tomographic images, functional, structural, and motion data are combined for a patient-specific model. Experiments on synthetic and clinical data show the importance of image-derived motion on estimating pathophysiologically plausible mechanical properties and the promising performance of our framework. From seven patient data sets, the recall, precision, Dice coefficient, relative volume difference, and average surface distance between the personalized tumor growth simulations and the measurements were 83.2 +/- 8.8%, 86.9 +/- 8.3%, 84.4 +/- 4.0%, 13.9 +/- 9.8%, and 0.6 +/- 0.1 mm, respectively.
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