Progressive cerebral atrophy is a physical component of the most common forms of dementia - Alzheimer's disease, vascular dementia, Lewy-Body disease and fronto-temporal dementia. We propose a phenomenological simulation of atrophy in MR images that provides gold-standard data; the origin and rate of progression of atrophy can be controlled and the resultant remodelling of brain structures is known. We simulate diffuse global atrophic change by generating global volumetric change in a physically realistic biomechanical model of the human brain. Thermal loads are applied to either single, or multiple, tissue types within the brain to drive tissue expansion or contraction. Mechanical readjustment is modelled using finite element methods (FEM). In this preliminary work we apply these techniques to the MNI brainweb phantom to produce new images exhibiting global diffuse atrophy. We compare the applied atrophy with that measured from the images using an established quantitative technique. Early results are encouraging and suggest that the model can be extended and used for validation of atrophy measurement techniques and non-rigid image registration, and for understanding the effect of atrophy on brain shape.
The generation of patient specific meshes for Finite Element Methods (FEM) with application to brain deformation is a time consuming process, but is essential for the modeling of intra-operative deformation of the brain during neurosurgery using FEM techniques. We present an automatic method for the generation of FEM meshes fitting patient data. The method is based on non-rigid registration of patient MR images to an atlas brain image, followed by deformation of a high-quality mesh of this atlas brain. We demonstrate the technique on brain MRI images from 12 patients undergoing neurosurgery. We show that the FEM meshes generated by our technique are of good quality. We then demonstrate the utility of these FEM meshes by simulating simple neuro-surgical scenarios on example patients, and show that the deformations predicted by our brain model match the observed deformations. The meshes generated by our technique are of good quality, and are suitable for modeling the types of deformation observed during neurosurgery. The deformations predicted by a simple loading scenario match well with those observed following the actual surgery. This paper does not attempt an exhaustive study of brain deformation, but does provide an essential tool for such a study - a method of rapidly generating Finite Element Meshes fitting individual subject brains.
We present initial results from evaluating the accuracy with which biomechanical breast models based on finite element methods can predict the displacements of tissue within the breast. We investigate the influence of different tissue elasticity values, Poisson's ratios, boundary conditions, finite element solvers and mesh resolutions on one data set. MR images were acquired before and after compressing a volunteer's breast gently. These images were aligned using a 3D non-rigid registration algorithm. The boundary conditions were derived from the result of the non-rigid registration or by assuming no patient motion at the deep or medial side. Three linear and two non-linear elastic material models were tested. The accuracy of the BBMs was assessed by the Euclidean distance of twelve corresponding anatomical landmarks. Overall, none of the tested material models was obviously superior to another regarding the set of investigated values. A major average error increase was noted for partially inaccurate boundary conditions at high Poisson's ratios due to introduced volume change. Maximal errors remained, however, high for low Poisson's ratio due to the landmarks closeness to the inaccurate boundary conditions. The choice of finite element solver or mesh resolution had almost no effect on the performance outcome.
This work presents a validation study for non-rigid registration of 3D contrast enhanced magnetic resonance mammography images. We are using our previously developed methodology for simulating physically plausible, biomechanical tissue deformations using finite element methods to compare two non-rigid registration algorithms based on single-level and multi-level free-form deformations using B-splines and normalized mutual information. We have constructed four patient-specific finite element models and applied the solutions to the original post-contrast scans of the patients, simulating tissue deformation between image acquisitions. The original image pairs were registered to the FEM-deformed post-contrast images using different free-form deformation mesh resolutions. The target registration error was computed for each experiment with respect to the simulated gold standard on a voxel basis. Registration error and single-level free-form deformation resolution were found to be intrinsically related: the smaller the spacing, the higher localized errors, indicating local registration failure. For multi-level free-form deformations, the registration errors improved for increasing mesh resolution. This study forms an important milestone in making our non-rigid registration framework applicable for clinical routine use.