Biomechanical model for computing deformations for whole‐body image registration: A meshless approach. (14th March 2016)

• Record Type: Journal Article
• Authors: Li, Mao; Miller, Karol; Joldes, Grand Roman; Kikinis, Ron; Wittek, Adam
• Is Part Of: International journal for numerical methods in biomedical engineering. Volume 32:Number 12(2016:Dec.)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-03-14
• Subjects: patient‐specific biomechanical modelling -- whole‐body image registration -- meshless model -- Hausdorff distance -- meshless methods; Biomedical engineering -- Periodicals; Imaging systems in medicine -- Periodicals; Numerical analysis -- Periodicals; Engineering mathematics -- Periodicals; 610.28
• DOI: 10.1002/cnm.2771
• Languages: English
• ISSNs: 2040-7939

Abstract

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. Abstract : Meshless discretisation with fuzzy tissue classification to assign constitutive properties at the integration points of the computational grid was applied to create nonlinear biomechanical models for computing 3D deformations for whole‐body image registration. This fuzzy meshless framework eliminates time‐consuming meshing and most image segmentation required by traditional approach that relies on finite element discretisation to create patient‐specific biomechanical models. Quantitative evaluation using edge‐based Hausdorff distance shows that the proposed fuzzy meshless framework can successfully register vast majority of the image features.