Keywords: Analysis/Processing, Liver, Machine learning, quantitative imaging
Motivation: Extensive recent work has been devoted to quantitative MRI, but practical implementation of quantitative parameter mapping is hindered by the lack of tools for easy visualization and automated analysis.
Goal(s): We demonstrate the applicability of a self-supervised contrastive pretraining framework for organ segmentation in automated analysis of free-breathing 3D liver T1 mapping.
Approach: A DL model is pretrained to learn T1 contrast information from multi-contrast images acquired for T1 parameter mapping.
Results: With few labeled examples, an organ segmentation framework was developed, and its utility in interpreting parameter maps was demonstrated.
Impact: Multi-contrast information from images typically acquired for parameter estimation in quantitative MRI can be leveraged to pretrain organ segmentation models with self-supervision, enabling automated analysis of quantitative parameter maps.
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