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Abstract #1101

Synthetic Image-Derived Phenotypes Using Physics-based Simulations: Predicting Variability in Subcortical Volumes

Hongyan Liu1, Nikos Priovoulos1, Frederik J. Lange1, Gaurav Bhalerao1, Stephen M. Smith1, Karla L. Miller1, and Aaron T. Hess1
1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

Synopsis

Keywords: Synthetic MR, Simulation/Validation, Synthetic MR; Segmentation

Motivation: MR image-derived phenotypes (IDPs), such as subcortical volumes, vary widely across acquisition protocols, complicating comparisons in large cohort studies.

Goal(s): To develop a physics-based simulation pipeline to synthesize IDPs, helping predict how different protocols affect IDP variability.

Approach: Given quantitative relaxometry maps and sequence parameters, this abstract proposes a pipeline for synthesizing IDPs using MR physics-based simulations, which outputs both the synthetic T1w images and FSL-segmented deep grey matter (DGM) structure volumes.

Results: Simulation experiments preliminary validate the synthesization pipeline. Experiments show synthesised DGM volume variations my be induced by variations in acquisition protocols and varying protocol sensitivities to partial volume effects.

Impact: This pipeline is a first step in using image synthesis to generate matched IDPs precisely. It offers a foundation for exploring protocol sensitivity in neuroimaging biomarkers and can serve as a basis for addressing cross-protocol data harmonization challenges.

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