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Sensitivity of Tumor Motion Simulation Accuracy to Lung Biomechanical Modeling Approaches and Parameters

J Nasehi Tehrani

j nasehi tehrani1*, Y Yang2 , R Werner3 , W Lu4 , D Low5 , X Guo6 , J Wang1 , (1) UT Southwestern Medical Center, Dallas, Texas, (2) University of New Mexico, Albuquerque, New Mexico, (3) University Medical Center Hamburg-Eppendorf, Hamburg, (4) University of Maryland School of Medicine, Baltimore, MD, (5) University of California, Los Angeles, CA, (6) University of Texas, Richardson, Texas


TH-CD-303-3 (Thursday, July 16, 2015) 10:00 AM - 12:00 PM Room: 303

Purpose: In this study, lung and tumor are modeled with separate materials and three types of material with their biomechanical parameters were systematically investigated to minimize the tumor center of mass (TCM) motion simulation error between two respiratory phases.
Methods: Tetrahedral meshes of the lungs and the tumors were created and biomechanical parameters are assigned to them as two separate homogenous materials. Three different linear and nonlinear hyperelastic models are investigated, including linear isotropic elastic model, Neo-Hookean compressible material model, and Uncoupled Mooney-Rivlin material model. The registration has been done between the end-expiration (phase 50%) and maximal lung deformation (phase 0%) 4D-CT using the demons registration algorithm and the lung surface deformation vector field (SDVF) was extracted from voxel DVFs as boundary conditions for the finite element model (FEM). A Quasi-Newton FEA was performed to simulate displacements of the lung and the tumor. The patient-specific optimal parameters were estimated by minimizing the TCM motion simulation error in eight patients.
Results: Among the three investigated models, the uncoupled Mooney-Rivlin material shows highest TCM simulation accuracy. The TCM motion simulation error shows more sensitivity to the lung biomechanical parameters in comparison to the tumor parameters. The average TCM simulation error for the Mooney-Rivlin material model along left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions is 0.80mm, 0.86mm, and 1.51mm, respectively.
Conclusion: Finite element analysis (FEA) based biomechanical modeling can be used to predict lung respiratory motion. In FEA-based biomechanical modeling, material and biomechanical parameters are two important factors that can improve the modeling accuracy. The proposed strategy provides a reliable way to estimate patient-specific biomechanical parameters in FEM for lung tumor motion simulation.

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