Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model [MRI]

Abstract

A biomechanical model of the brain is presented, using a finite-element formulation. Emphasis is given to the modeling of the soft-tissue deformations induced by the growth of tumors and its application to the registration of anatomical atlases, with images from patients presenting such pathologies. First, an estimate of the anatomy prior to the tumor growth is obtained through a simulated biomechanical contraction of the tumor region. Then a normal-to-normal atlas registration to this estimated pre-tumor anatomy is applied. Finally, the deformation from the tumor-growth model is applied to the resultant registered atlas, producing an atlas that has been deformed to fully register to the patient images. The process of tumor growth is simulated in a nonlinear optimization framework, which is driven by anatomical features such as boundaries of brain structures. The deformation of the surrounding tissue is estimated using a nonlinear elastic model of soft tissue under the boundary conditions imposed by the skull, ventricles, and the falx and tentorium. A preliminary two-dimensional (2-D) implementation is presented in this paper, and tested on both simulated and patient data. One of the long-term goals of this work is to use anatomical brain atlases to estimate the locations of important brain structures in the brain and to use these estimates in presurgical and radiosurgical planning systems.

Serial registration of intraoperative MR images of the brain

“…linear elasticity models discretized using the Finite Element (FE) Method) have been explicitly proposed to constrain image registration (Kyriacou et al, 1999;Hagemann et al, 1999). In these studies, the deformation from one image to the next is enforced by manually, or semi-automatically defining correspondences between contours in the images to register.…”

Dense deformation field estimation for atlas-based segmentation of pathological MR brain images

“…Kyriacou and Davatzikos [3] proposed a biomechanical model of the brain using a finite-element method. They first model soft tissue deformations induced by the tumor growth and, then, they registered the anatomical atlas with a transformed patient image from which the tumor was removed.…”

Tumor Growth Modeling in Oncological Image Analysis

“…With this they relaxed the incompressibility assumption made in [13] and ignored the viscous effect, keeping in mind that times related to deformations was very large compared to viscosity time constants. In addition to modeling the mass effect due to bulk tumor growth they have also taken into account the expansion caused by the edema and the fact that part of the mass effect should be attributed to edema.…”

“…In [13], Kyriacou et al assumed that brain tissue can be better characterized by a nonlinear elastic material than a linear one. They modelled white, grey and tumor tissue as nonlinear elastic solids obeying equations of an imcompressible nonlinearly elastic neo-Hookean model.…”

“…Proposed algorithms use these model parameters in separating the deformation field between the atlas and the patient into inter-subject variation and tumorinduced parts during the registration process. Kyriacou et al proposed one of the first atlas to patient registration algorithms based on the tumor growth dynamics [13]. Starting from the patient image, their algorithm first simulates the biomechanical contraction in the case of the removal of the tumor to estimate patient anatomy prior to the tumor.…”