Generative diffeomorphic atlas construction from brain and spinal cord MRI data

TL;DR

This paper presents a Bayesian hierarchical generative model for integrated analysis of brain and spinal cord MRI data, enabling comprehensive CNS morphology studies without organ-specific tools.

Contribution

It introduces a unified framework that models both brain and spinal cord data simultaneously, improving integration and generalization over existing organ-specific methods.

Findings

Allows joint CNS analysis from MRI data

Captures anatomical variability across populations

Extends existing segmentation approaches

Abstract

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.