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Deep learning–based MR fingerprinting ASL ReconStruction (DeepMARS)

Research output: Contribution to journalArticle

Qiang Zhang, Pan Su, Zhensen Chen, Ying Liao, Shuo Chen, Rui Guo, Haikun Qi, Xuesong Li, Xue Zhang, Zhangxuan Hu, Hanzhang Lu, Huijun Chen

Original languageEnglish
Pages (from-to)1024-1034
Number of pages11
JournalMagnetic Resonance in Medicine
Volume84
Issue number2
DOIs
Publication statusPublished - 1 Aug 2020

King's Authors

Abstract

Purpose: To develop a reproducible and fast method to reconstruct MR fingerprinting arterial spin labeling (MRF-ASL) perfusion maps using deep learning. Method: A fully connected neural network, denoted as DeepMARS, was trained using simulation data and added Gaussian noise. Two MRF-ASL models were used to generate the simulation data, specifically a single-compartment model with 4 unknowns parameters and a two-compartment model with 7 unknown parameters. The DeepMARS method was evaluated using MRF-ASL data from healthy subjects (N = 7) and patients with Moymoya disease (N = 3). Computation time, coefficient of determination (R2), and intraclass correlation coefficient (ICC) were compared between DeepMARS and conventional dictionary matching (DM). The relationship between DeepMARS and Look–Locker PASL was evaluated by a linear mixed model. Results: Computation time per voxel was <0.5 ms for DeepMARS and >4 seconds for DM in the single-compartment model. Compared with DM, the DeepMARS showed higher R2 and significantly improved ICC for single-compartment derived bolus arrival time (BAT) and two-compartment derived cerebral blood flow (CBF) and higher or similar R2/ICC for other parameters. In addition, the DeepMARS was significantly correlated with Look–Locker PASL for BAT (single-compartment) and CBF (two-compartment). Moreover, for Moyamoya patients, the location of diminished CBF and prolonged BAT shown in DeepMARS was consistent with the position of occluded arteries shown in time-of-flight MR angiography. Conclusion: Reconstruction of MRF-ASL with DeepMARS is faster and more reproducible than DM.

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