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A Systematic Post-Processing Approach for Quantitative $T_{1ρ}$ Imaging of Knee Articular Cartilage
Objective: To establish an automated pipeline for post-processing of quantitative spin-lattice relaxation time constant in the rotating frame ($T_{1\rho}$) imaging of knee articular cartilage.
Chemical Shift Encoding based Double Bonds Quantification in Triglycerides using Deep Image Prior
Fatty acid can potentially serve as biomarker for evaluating metabolic disorder and inflammation condition, and quantifying the double bonds is the key for revealing fatty acid information.
An Uncertainty Aided Framework for Learning based Liver $T_1ρ$ Mapping and Analysis
Approach: To address this need, we propose a parametric map refinement approach for learning-based $T_1\rho$ mapping and train the model in a probabilistic way to model the uncertainty.
Uncertainty-Aware Self-supervised Neural Network for Liver $T_{1ρ}$ Mapping with Relaxation Constraint
Epistemic uncertainty and aleatoric uncertainty are modelled for the $T_{1\rho}$ quantification network to provide a Bayesian confidence estimation of the $T_{1\rho}$ mapping.
