Probabilistic framework for subject-specific and population-based analysis of longitudinal changes and disease progression in brain MR images

Aging and many neurological diseases cause progressive changes in brain morphology. Both subject-specific detection and measurement of these changes, as well as their population-based analysis are of great interest in many clinical studies. Generally, both problems are handled separately. However, as population-based knowledge facilitates subject-specific analysis and vice versa, we propose a unified statistical framework for subject-specific and population-based analysis of longitudinal brain MR image sequences of subjects suffering from the same neurological disease. The proposed method uses a maximum a posteriori formulation and the expectation maximization algorithm to simultaneously and iteratively segment all images in separate tissue classes, construct a global probabilistic 3D brain atlas and non-rigidly deform the atlas to each of the images to guide their segmentation. In order to enable a population-based analysis of the disease progression, an intermediate 4D probabilistic brain atlas is introduced, representing a discrete set of disease progression stages. The 4D atlas is simultaneously constructed with the 3D brain atlas by incorporating assignments of each input image (voxelwise) to a particular disease progression stage in the statistical framework. Moreover, these assignments enable both temporal and spatial subject-specific disease progression analysis. This includes detecting delayed or advanced disease progression and indicating the affected regions. The method is validated on a publicly available data set on which it shows promising results.