Keywords: Analysis/Processing, Quantitative Imaging, phase-cycled bSSFP, multi-parametric mapping, uncertainty quantification
Motivation: When using black-box regression models in diagnostic imaging, it is critical to quantify the uncertainty of such model predictions.
Goal(s): Implementing and understanding uncertainty quantification for multi-parametric quantitative MRI. Do prediction intervals reflect model uncertainty?
Approach: Train conditional quantile regression deep neural networks with subsequent conformalization steps for multi-parametric quantitative mapping without making distributional assumptions about the data.
Results: Conformalized relaxometry and magnetic field prediction intervals reflect model uncertainty. Conformalized quantile regression was successfully implemented and provides supportive information about intrinsic model uncertainty which is mandatory for clinical decision making.
Impact: A novel method for quantifying uncertainty of supervised machine learning models for multi-parametric quantitative MRI was successfully tested in silico and in vivo. Conformalized quantile regression allows prediction of confidence intervals without making assumptions about the training data distribution.
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