An Uncertainty Aided Framework for Learning based Liver T_1ρ Mapping and Analysis
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Objective: Quantitative T_1ρ imaging has potential for assessment of biochemical alterations of liver pathologies. Deep learning methods have been employed to accelerate quantitative T_1ρ imaging. To employ artificial intelligence-based quantitative imaging methods in complicated clinical environment, it is valuable to estimate the uncertainty of the predicated T_1ρ 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_1ρ 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_1ρ mapping network to further improve the mapping performance and to remove pixels with unreliable T_1ρ 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 uncertainty reflects the actual error level, and it can be used to further reduce relative T_1ρ mapping error to 2.60 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_1ρ mapping of the liver.
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