Deep Learning Improves Robustness of Voxelwise Kinetic Modeling for Hyperpolarized Carbon-13 MRI.
TL;DR
NLLS is effective for k PL estimation under ideal model-matched conditions, whereas the NN provides more stable voxelwise maps, especially for weakly identifiable parameters and under low-SNR or in vivo conditions.
Abstract
Purpose
To evaluate whether deep learning improves the robustness of voxelwise kinetic parameter estimation from hyperpolarized (HP) 13C MRI compared with nonlinear least-squares (NLLS) fitting.
Methods
A hybrid neural network (NN) was trained on synthetic pyruvate/lactate time courses generated from an open-system two-compartment HP 13C signal model to estimate the pyruvate-to-lactate conversion rate ( k PL ), vascular-extravascular exchange rate ( k VE ), and vascular volume fraction ( v B ). NN performance was compared with NLLS across flip-angle schemes, SNR levels, perturbations in acquisition parameters, and in vivo. Matched-ratio simulations tested whether model-estimated ( k PL ) retained information beyond the Lac/Pyr area-under-the-curve ratio, AUC Lac / Pyr = AUC Lac / AUC Pyr .
Results
In simulations, NLLS and NN performance were comparable for k PL estimation at high SNR, whereas the NN outperformed NLLS at low SNR and for the weakly identifiable parameters k VE and v B . In vivo, NN maps were more spatially coherent than NLLS maps: k PL corresponded with AUC Lac / Pyr , while k VE and v B corresponded with pyruvate AUC. In matched-ratio simulations, NLLS-derived k PL discriminated the metabolic classes better than NN-derived k PL , although both model-based estimates retained discriminatory information.
Conclusion
NLLS is effective for k PL estimation under ideal model-matched conditions, whereas the NN provides more stable voxelwise maps, especially for weakly identifiable parameters and under low-SNR or in vivo conditions. Prospective biological or repeatability validation is needed to establish quantitative accuracy.