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Abstract #4314

Enhancing Reliability of MRI-based Brain Morphometry by Synthetic MPRAGE Generation

Timo Blattner1, Richard McKinley1, Roland Wiest1, Christian Rummel1,2, and Milena Capiglioni1
1Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland, 2European Campus Rottal-Inn, Deggendorf Institute of Technology, Pfarrkirchen, Germany

Synopsis

Keywords: Segmentation, AI/ML Software, Morphometry, Contrast-invariance, Signal Modeling, Deep Learning

Motivation: Brain morphometry is increasingly recognized as potential biomarker for tracking neurodegenerative disease progression. However, variations in MRI acquisition parameters, common in clinical practice, compromise the reliability of morphometric measures.

Goal(s): To achieve contrast-invariant morphometry across MPRAGE images with varying grey/white matter contrasts.

Approach: We retrained a deep-learning-based segmentation tool (DL+DiReCT) by modeling MPRAGE contrast variations as a function of MRI parameters (TI and TR). Cortical thickness variability was assessed before and after retraining.

Results: The retrained model improved contrast invariance, reducing the contrast-related cortical thickness dependence from 5% to 2% across clinically relevant parameters.

Impact: We created a contrast-invariant segmentation tool that improves brain morphometry accuracy across variable MRI settings, enabling more reliable monitoring of neurodegenerative disease progression. This tool improves assessment accuracy across longitudinal and multi-parameter MRI acquisitions common in clinical practice.

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Keywords