Spatially Constrained Estimation of Hyperpolarized 13C MRI Pharmacokinetic Rate Constant Maps using a U-Net

Motivation: Current kPL fitting methods for Hyperpolarized [1-13C]Pyruvate MRI data are focused on voxel-wise models that do not consider spatial relationships. Incorporating spatial constraints may improve kPL accuracy for noisy data. Goal(s): The goal of this study was to use a U-net to fit kPL, the pyruvate-to-lactate conversion rate, where the convolutional layers impose spatial constraints. Approach: Simulated data of Hyperpolarized 13C-Pyruvate including perfusion and 13C-lactate conversion with random spatial augmentation and noise was used to train a U-net. Results: The U-net kPL estimation showed advantage over voxel-wise methods in the low SNR regime and performance was heavily influenced by the training data. Impact: Using a U-Net to estimate kPL maps for Hyperpolarized 13C-Pyruvate MRI data will aid the field in optimizing quantitative methods for future clinical use and serve as a proof-of-concept of using deep learning to estimate kinetic rates.