Objective: Quantitative T1βΟ imaging has potential for assessment of
biochemical alterations of liver pathologies. Deep learning methods have been
employed to accelerate quantitative T1βΟ imaging. To employ artificial
intelligence-based quantitative imaging methods in complicated clinical
environment, it is valuable to estimate the uncertainty of the predicated
T1βΟ values to provide the confidence level of the quantification results.
The uncertainty should also be utilized to aid the post-hoc quantitative
analysis and model learning tasks. Approach: To address this need, we propose a
parametric map refinement approach for learning-based T1βΟ mapping and
train the model in a probabilistic way to model the uncertainty. We also
propose to utilize the uncertainty map to spatially weight the training of an
improved T1βΟ mapping network to further improve the mapping performance
and to remove pixels with unreliable T1βΟ values in the region of
interest. The framework was tested on a dataset of 51 patients with different
liver fibrosis stages. Main results: Our results indicate that the
learning-based map refinement method leads to a relative mapping error of less
than 3% and provides uncertainty estimation simultaneously. The estimated
uncertainty reflects the actual error level, and it can be used to further
reduce relative T1βΟ mapping error to 2.60% as well as removing unreliable
pixels in the region of interest effectively. Significance: Our studies
demonstrate the proposed approach has potential to provide a learning-based
quantitative MRI system for trustworthy T1βΟ mapping of the liver