Deep learning–based MR fingerprinting ASL ReconStruction (DeepMARS)

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 us...

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Veröffentlicht in:	Magnetic resonance in medicine 2020-08, Vol.84 (2), p.1024-1034
Hauptverfasser:	Zhang, Qiang, Su, Pan, Chen, Zhensen, Liao, Ying, Chen, Shuo, Guo, Rui, Qi, Haikun, Li, Xuesong, Zhang, Xue, Hu, Zhangxuan, Lu, Hanzhang, Chen, Huijun
Format:	Artikel
Sprache:	eng
Schlagworte:	Angiography Arteries Blood flow Cerebral blood flow Cerebrovascular Circulation Computer simulation Computing time Correlation coefficient Correlation coefficients Deep Learning DeepMARS Fingerprinting Humans Machine learning Magnetic Resonance Imaging Mathematical models Moyamoya Disease MRF‐ASL Neural networks Parameters Perfusion Random noise Reconstruction Reproducibility Spin labeling Spin Labels
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