#Apply rigid body rotations postview registration#

In this study, 3-D comprehensive patient-specific non-linear biomechanical models implemented using Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithms are applied to predict a 3-D deformation field for whole-body image registration. However, applications so far have been limited to 2-D models and computing single organ deformations. Meshless discretisation has been proposed to solve this challenge. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time consuming.

#Apply rigid body rotations postview registration#

Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. Li, Mao Miller, Karol Joldes, Grand Roman Kikinis, Ron Wittek, Adam Finally, the authors also discuss a new approach to generate kVCT images representing the deformed anatomy that accounts for gaps and antialiasing artifactsīiomechanical Model for Computing Deformations for Whole-Body Image Registration: A Meshless Approach

Physiological changes representing tumor regression were simulated by reducing the target volume and enabling the surrounding soft structures to deform accordingly. Posture changes were simulated by articulating the skeletal structure and enabling the soft structures to deform accordingly. The soft tissue deformation for a given skeletal actuation was performed using an implicit Euler integration with each iteration split into two substeps: one for the muscle structures and the other for the remaining soft tissues. Within a given muscle structure, the voxels were classified using a uniform grid and a normalized mass was assigned to each voxel based on its Hounsfield number. While the bony anatomy was modeled as rigid body, the muscle and soft tissue structures were modeled as massâ€“spring-damper models with elastic material properties that corresponded to the underlying contoured anatomies. The voxels inside amoreÂ Â» given 3D contour boundary were clustered using a graphics processing unit (GPU) based algorithm that accounted for inconsistencies and gaps in the boundary to form a volumetric structure. Methods: The biomechanical model was developed using HN kVCT datasets and the corresponding structure contours. The authors present a methodology for generating biomechanically realistic ground-truth data for validating DIR algorithms for HN anatomy by (a) developing a high-resolution deformable biomechanical HN model from a planning CT, (b) simulating deformations for a range of interfraction posture changes and physiological regression, and (c) generating subsequent CT images representing the deformed anatomy. Purpose: Validating the usage of deformable image registration (DIR) for daily patient positioning is critical for adaptive radiotherapy (RT) applications pertaining to head and neck (HN) radiotherapy. A GPU based high-resolution multilevel biomechanical head and neck model for validating deformable image registration