Patient‐specific computational biomechanics of the brain without segmentation and meshing

Zhang, Johnny Y.; Joldes, Grand Roman; Wittek, Adam; Miller, Karol

doi:10.1002/cnm.2507

Cited by 35 publications

(76 citation statements)

References 56 publications

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“…Assignment of material properties directly from the medical images using fuzzy tissue classification was successfully applied in the context of computation of the brain and abdominal organ deformations when treating soft tissues as a hyperelastic neo-Hookean material. 64,115 …”

Section: Beyond Finite Element Meshes: Meshless Methods and Models Asmentioning

confidence: 98%

From Finite Element Meshes to Clouds of Points: A Review of Methods for Generation of Computational Biomechanics Models for Patient-Specific Applications

et al. 2015

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It has been envisaged that advances in computing and engineering technologies could extend surgeons' ability to plan and carry out surgical interventions more accurately and with less trauma. The progress in this area depends crucially on the ability to create robustly and rapidly patientspecific biomechanical models. We focus on methods for generation of patient-specific computational grids used for solving partial differential equations governing the mechanics of the body organs. We review state-of-the-art in this area and provide suggestions for future research. To provide a complete picture of the field of patient-specific model generation, we also discuss methods for identifying and assigning patient-specific material properties of tissues and boundary conditions.

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Section: Beyond Finite Element Meshes: Meshless Methods and Models Asmentioning

confidence: 98%

From Finite Element Meshes to Clouds of Points: A Review of Methods for Generation of Computational Biomechanics Models for Patient-Specific Applications

et al. 2015

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“…A Fuzzy C-Means (FCM) algorithm is adopted here to classify tissues and assign material properties automatically without the image segmentation for each organ [14]. The key step of the algorithm is to build the relationships between tissues and image intensity values.…”

Section: Materials Propertiesmentioning

confidence: 99%

“…The key step of the algorithm is to build the relationships between tissues and image intensity values. The FCM algorithm divides image intensity into different groups by computing the membership function between each pixel and all the specified cluster centres, and minimising the objective function [14]. …”

Section: Materials Propertiesmentioning

confidence: 99%

Whole-Body Image Registration Using Patient-Specific Nonlinear Finite Element Model

Wittek

Joldes

et al. 2014

Computational Biomechanics for Medicine

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Registration of whole-body radiographic images is an important task in analysis of the disease progression and assessment of responses to therapies. Numerous registration algorithms have been successfully used in applications where differences between source and target images are relatively small. However, registration of whole-body CT scans remains extremely challenging for such algorithms as it requires taking large deformations of body organs and articulated skeletal motions into account. For registration problems involving large differences between source and target images, registration using biomechanical models has been recommended in the literature. Therefore, in this study, we propose a patientspecific nonlinear finite element model to predict the movements and deformations of body organs for the whole-body CT image registration. We conducted a verification example in which a patient-specific torso model was implemented using a suite of nonlinear finite element algorithms we previously developed, verified and successfully used in neuroimaging registration. When defining the patientspecific geometry for the generation of computational grid for our model, we abandoned the time-consuming hard segmentation of radiographic images typically used in patient-specific biomechanical modelling to divide the body into nonoverlapping constituents with different material properties. Instead, an automated Fuzzy C-Means (FCM) algorithm for tissue classification was applied to assign the constitutive properties at finite element mesh integration points. The loading was defined as a prescribed displacement of the vertebrae (treated as articulated rigid bodies) between the two CT images. Contours of the abdominal organs obtained by warping the source image using the deformation field within the body predicted using our patient-specific finite element model differed by only up to only two voxels from the actual organs' contours in the target image. These results can be regarded as encouraging step in confirming feasibility of conducting accurate registration of whole-body CT images using nonlinear finite element models without the necessity for time-consuming image segmentation when building patientspecific finite element meshes.

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“…Refs. 59, 81), alternative numerical methods (e.g., Refs. 47, 74), and reconstructive approaches (e.g., Refs.…”

Section: Role Of Patient-specific Computational Modeling Towards Braimentioning

confidence: 99%

Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery

Miga

2015

Ann Biomed Eng

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With the recent advances in computing, the opportunities to translate computational models to more integrated roles in patient treatment are expanding at an exciting rate. One area of considerable development has been directed towards correcting soft tissue deformation within image guided neurosurgery applications. This review captures the efforts that have been undertaken towards enhancing neuronavigation by the integration of soft tissue biomechanical models, imaging and sensing technologies, and algorithmic developments. In addition, the review speaks to the evolving role of modeling frameworks within surgery and concludes with some future directions beyond neurosurgical applications.

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Patient‐specific computational biomechanics of the brain without segmentation and meshing

Cited by 35 publications

References 56 publications

From Finite Element Meshes to Clouds of Points: A Review of Methods for Generation of Computational Biomechanics Models for Patient-Specific Applications

From Finite Element Meshes to Clouds of Points: A Review of Methods for Generation of Computational Biomechanics Models for Patient-Specific Applications

Whole-Body Image Registration Using Patient-Specific Nonlinear Finite Element Model

Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery

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