Patient-specific biomechanical model as whole-body CT image registration tool

Abstract

Whole-body computed tomography (CT) image registration is important for cancer diagnosis, therapy planning and treatment. Such registration requires accounting for large differences between source and target images caused by deformations of soft organs/tissues and articulated motion of skeletal structures. The registration algorithms relying solely on image processing methods exhibit deficiencies in accounting for such deformations and motion. We propose to predict the deformations and movements of body organs/tissues and skeletal structures for whole-body CT image registration using patient-specific non-linear biomechanical modelling. Unlike the conventional biomechanical modelling, our approach for building the biomechanical models does not require time-consuming segmentation of CT scans to divide the whole body into non-overlapping constituents with different material properties. Instead, a Fuzzy C-Means (FCM) algorithm is used for tissue classification to assign the constitutive properties automatically at integration points of the computation grid. We use only very simple segmentation of the spine when determining vertebrae displacements to define loading for biomechanical models. We demonstrate the feasibility and accuracy of our approach on CT images of seven patients suffering from cancer and aortic disease. The results confirm that accurate whole-body CT image registration can be achieved using a patient-specific non-linear biomechanical model constructed without time-consuming segmentation of the whole-body images.

In silico comparison of photons versus carbon ions in single fraction therapy of lung cancer

“…To date, there are more than 500 publications that have used Slicer in their research [Slicer, 2016b], with topic ranging from teaching [Pujol et al, 2016], disease staging [Liu et al, 2015, Liu et al, 2016a, motion tracking [Behringer et al, 2015] to image reconstruction [Meyer et al, 2015], image registration [Li et al, 2015b, Li et al, 2015a and others. Slicer offers ample functionalities and is especially suited for research, since it can be modified to specific needs.…”

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

“…For applications (such as image-guided surgery) that can be formulated as Dirichlet-type problems, it is possible to determine deformations of soft organs during surgery without exact knowledge of patient specific properties of soft tissues. 64,76,77,108 Using sophisticated inverse solution procedures it is also possible to compute wall stresses of aneurysms and other approximately statically determinate structures without the knowledge of the tissue properties. 56 Representation of boundary conditions is a key component of computational model formulation.…”

“…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 …”

“…Therefore, it is possible to determine deformations of soft organs during surgery without knowledge of patient-specific properties of tissues. 64,79,108 The second type of problems has been identified in the field of biomechanics of intracranial aneurysms that can be approximately modeled as pressure vessels that are known to be statically determinate. If we formulate the boundary value problem inversely, we are able to determine the aneurysm wall stress distribution without knowledge of patientspecific mechanical properties of the tissues comprising the wall.…”

Biomechanical model for computing deformations for whole‐body image registration: A meshless approach

“…In our previous studies, we proposed the following solutions to eliminate tedious image segmentation and mesh generation when creating biomechanical models for computing organ deformations for image registration: To assign material properties using a fuzzy tissue classification membership function without the need for image segmentation . To use meshless (also known as mesh free) methods of computational mechanics that utilise an unstructured cloud of points for spatial discretisation and are therefore much less demanding when building computational grids than finite element discretisation . …”