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Abstract #3063

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

Sule Sahin1,2, Zhen Jane Wang1, and Peder E.Z. Larson1,2
1Radiology, University of California, San Francisco, San Francisco, CA, United States, 2UC Berkeley - UCSF Graduate Program in Bioengineering, Berkeley, CA, United States

Synopsis

Keywords: Hyperpolarized MR (Non-Gas), Simulations

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.

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