Enhanced parameter estimation in multiparametric arterial spin labeling using artificial neural networks.

PURPOSE

Multiparametric arterial spin labeling (MP-ASL) can quantify cerebral blood flow (CBF) and arterial cerebral blood volume (CBV). However, its accuracy is compromised owing to its intrinsically low SNR, necessitating complex and time-consuming parameter estimation. Deep neural networks (DNNs) offer a solution to these limitations. Therefore, we aimed to develop simulation-based DNNs for MP-ASL and compared the performance of a supervised DNN (DNN), physics-informed unsupervised DNN (DNN), and the conventional lookup table method (LUT) using simulation and in vivo data.

METHODS

MP-ASL was performed twice during resting state and once during the breath-holding task. First, the accuracy and noise immunity were evaluated in the first resting state. Second, CBF and CBV values were statistically compared between the first resting state and the breath-holding task using the Wilcoxon signed-rank test and Cliff's delta. Finally, reproducibility of the two resting states was assessed.

RESULTS

Simulation and first resting-state analyses demonstrated that DNN had higher accuracy, noise immunity, and a six-fold faster computation time than LUT. Furthermore, all methods detected task-induced CBF and CBV elevations, with the effect size being larger with the DNN (CBF, p = 0.055, Δ = 0.286; CBV, p = 0.008, Δ = 0.964) and DNN (CBF, p = 0.039, Δ = 0.286; CBV, p = 0.008, Δ = 1.000) than that with LUT (CBF, p = 0.109, Δ = 0.214; CBV, p = 0.008, Δ = 0.929). Moreover, all the methods exhibited comparable and satisfactory reproducibility.

CONCLUSION

DNN outperforms DNN and LUT with respect to estimation performance and computation time.