Keywords: Data Processing, Relaxometry, Neuroinflammation, Quantitative Imaging
Motivation: Quantifying voxel-wise deviations in T1 relaxation times from a normative atlas enables the characterization of brain microstructural pathology with high spatial resolution. So far, registration errors and long runtimes have constrained its clinical adoption.
Goal(s): Provide accurate T1 deviation maps for clinical use, with decreased runtimes, reduced registration errors, and enhanced interpretation using noise filtering.
Approach: An existing algorithm was improved by extending the normative cohort with multi-centric data, replacing the registration method with a deep-learning model, and implementing a novel iterative noise filtering technique.
Results: This approach substantially reduced processing time, improved spatial alignment, and reduced measurement noise in T1 deviation maps.
Impact: Improved spatial alignment and a novel iterative noise filtering technique enhanced efficiency, accuracy and interpretability of a pipeline characterizing voxel-wise abnormalities of quantitative T1 relaxation times, paving the way for clinical adoption of pathology characterization with single-subject T1 deviations.
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