An uncertainty aided framework for learning based liver T 1ρ mapping and analysis

Abstract

Objective. Quantitative T 1 ρ imaging has potential for assessment of biochemical alterations of liver pathologies. Deep learning methods have been employed to accelerate quantitative T 1 ρ imaging. To employ artificial intelligence-based quantitative imaging methods in complicated clinical environment, it is valuable to estimate the uncertainty of the predicated T 1 ρ 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 T 1 ρ 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 T 1 ρ mapping network to further improve the mapping performance and to remove pixels with unreliable T 1 ρ 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 T 1 ρ 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 T 1 ρ mapping of the liver.