Simulation of local and global atrophy in Alzheimer's disease studies.

We propose a method for atrophy simulation in structural MR images based on finite-element methods, providing data for objective evaluation of atrophy measurement techniques. The modelling of diffuse global and regional atrophy is based on volumetric measurements from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on tissue properties and simulating plausible deformations with finite-element methods. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh, and a partial volume tissue model is used to reduce the impact of the mesh discretization. An example of simulated data is shown and a visual evaluation protocol used by experts has been developed to assess the degree of realism of the simulated images. First results demonstrate the potential of the proposed methodology.

Simulation of acquisition artefacts in MR scans: effects on automatic measures of brain atrophy.

Automatic algorithms in conjunction with longitudinal MR brain images can be used to measure cerebral atrophy, which is particularly pronounced in several types of dementia. An atrophy simulation technique has been devised to facilitate validation of these algorithms. To make this model of atrophy more realistic we simulate acquisition artefacts which are common problems in dementia imaging: motion (both step and periodic motion) and pulsatile flow artefact. Artefacts were simulated by combining different portions of k-space from various modified image. The original images were 7 MR scans of healthy elderly controls, each of which had two levels of simulated atrophy. We investigate the effect of the simulated acquisition artefacts in atrophy measurements provided by an automatic technique, SIENA.

An inverse problem approach to the estimation of volume change.

We present a new technique for determining structure-by-structure volume changes, using an inverse problem approach. Given a pre-labelled brain and a series of images at different time-points, we generate finite element meshes from the image data, with volume change modelled by means of an unknown coefficient of expansion on a per-structure basis. We can then determine the volume change in each structure of interest using inverse problem optimization techniques. The proposed method has been tested with simulated and clinical data. Results suggest that the presented technique can be seen as an alternative for volume change estimation.