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Biomechanical model for computing deformations for whole‐body image registration: A meshless approach
Author(s) -
Li Mao,
Miller Karol,
Joldes Grand Roman,
Kikinis Ron,
Wittek Adam
Publication year - 2016
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.2771
Subject(s) - discretization , computer science , meshfree methods , hausdorff distance , artificial intelligence , segmentation , nonlinear system , image registration , image (mathematics) , computer vision , finite element method , algorithm , geometry , mathematics , mathematical analysis , physics , structural engineering , engineering , quantum mechanics
Summary 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. However, complex and irregular geometry of the body organs makes generation of patient‐specific biomechanical models very time‐consuming. Meshless discretisation has been proposed to solve this challenge. However, applications so far have been limited to 2D models and computing single organ deformations. In this study, 3D comprehensive patient‐specific nonlinear biomechanical models implemented using meshless Total Lagrangian explicit dynamics algorithms are applied to predict a 3D deformation field for whole‐body image registration. Unlike a conventional approach that requires dividing (segmenting) the image into non‐overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the fuzzy c‐means algorithm without the image segmentation. Verification indicates that the deformations predicted using the proposed meshless approach are for practical purposes the same as those obtained using the previously validated finite element models. To quantitatively evaluate the accuracy of the predicted deformations, we determined the spatial misalignment between the registered (i.e. source images warped using the predicted deformations) and target images by computing the edge‐based Hausdorff distance. The Hausdorff distance‐based evaluation determines that our meshless models led to successful registration of the vast majority of the image features. Copyright © 2016 John Wiley & Sons, Ltd.