Generative diffeomorphic atlas construction from brain and spinal cord MRI data
This work addresses the problem of integrating brain and spinal cord neuroimaging data for researchers and clinicians, offering a unified approach that could improve analysis efficiency, though it appears incremental as it extends existing segmentation methods.
The paper tackles the challenge of creating an integrated modeling framework for brain and spinal cord MRI data by developing a hierarchical generative model that captures signal intensities and anatomical shape variability across subjects, enabling quantitative in vivo investigation of CNS morphology without organ-specific tools.
In this paper we will focus on the potential and on the challenges associated with the development of an integrated brain and spinal cord modelling framework for processing MR neuroimaging data. The aim of the work is to explore how a hierarchical generative model of imaging data, which captures simultaneously the distribution of signal intensities and the variability of anatomical shapes across a large population of subjects, can serve to quantitatively investigate, in vivo, the morphology of the central nervous system (CNS). In fact, the generality of the proposed Bayesian approach, which extends the hierarchical structure of the segmentation method implemented in the SPM software, allows processing simultaneously information relative to different compartments of the CNS, namely the brain and the spinal cord, without having to resort to organ specific solutions (e.g. tools optimised only for the brain, or only for the spinal cord), which are inevitably harder to integrate and generalise.