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Biomechanical Model for Computing Deformations for Whole-body Image Registration: A Meshless Approach

1Intelligent Systems for Medicine Laboratory, School of Mechanical and Chemical Engineering, The University of Western Australia, Perth, Australia.
2Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publication Date:
Int J Numer Method Biomed Eng
Int J Numer Method Biomed Eng. 2016 Dec;32(12).
PubMed ID:
hausdorff distance, meshless methods, meshless model, patient-specific biomechanical modelling, whole-body image , registration
Appears in Collections:
U24 CA180918/CA/NCI NIH HHS/United States
U41 RR019703/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
Generated Citation:
Li M., Miller K., Joldes G.R., Kikinis R., Wittek A. Biomechanical Model for Computing Deformations for Whole-body Image Registration: A Meshless Approach. Int J Numer Method Biomed Eng. 2016 Dec;32(12). PMID: 26791945. PMCID: PMC4956599.
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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 2-D models and computing single organ deformations. 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. Unlike a conventional approach which requires dividing (segmenting) the image into non-overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the Fuzzy C-Means (FCM) 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.