Morphometry-Based Longitudinal Neurodegeneration Simulation with MR Imaging

TL;DR

This paper introduces a morphometry-based framework for simulating future neurodegeneration in MR images, leveraging longitudinal data and template matching to improve prediction accuracy.

Contribution

It presents a novel simulation method that uses voxel-based morphometry and template affinity to predict neurodegeneration progression from longitudinal MR images.

Findings

Outperforms state-of-the-art voxel-based methods in accuracy

Uses a leave-one-out strategy for validation

Effectively predicts future neurodegenerative changes

Abstract

We present a longitudinal MR simulation framework which simulates the future neurodegenerative progression by outputting the predicted follow-up MR image and the voxel-based morphometry (VBM) map. This framework expects the patients to have at least 2 historical MR images available. The longitudinal and cross-sectional VBM maps are extracted to measure the affinity between the target subject and the template subjects collected for simulation. Then the follow-up simulation is performed by resampling the latest available target MR image with a weighted sum of non-linear transformations derived from the best-matched templates. The leave-one-out strategy was used to compare different simulation methods. Compared to the state-of-the-art voxel-based method, our proposed morphometry-based simulation achieves better accuracy in most cases.