Cortical correspondence using entropy-based particle systems and local features

This paper presents a new method of constructing compact statistical point-based models of populations of human cortical surfaces with functions of spatial locations driving the correspondence optimization. The proposed method is to establish a tradeoff between an even sampling of the surfaces (a low surface entropy) and the similarity of corresponding points across the population (a low ensemble entropy). The similarity metric, however, isn’t constrained to be just spatial proximity, but can be any function of spatial location, thus allowing the integration of local cortical geometry as well as DTI connectivity maps and vasculature information from MRA images. This method does not require a spherical parameterization or fine tuning of parameters. Experimental results are also presented, showing lower local variability for both sulcal depth and cortical thickness measurements, compared to other commonly used methods such as FreeSurfer.