Deep learning–based MR fingerprinting ASL ReconStruction (DeepMARS). Issue 2 (4th February 2020)
- Record Type:
- Journal Article
- Title:
- Deep learning–based MR fingerprinting ASL ReconStruction (DeepMARS). Issue 2 (4th February 2020)
- Main Title:
- Deep learning–based MR fingerprinting ASL ReconStruction (DeepMARS)
- Authors:
- Zhang, Qiang
Su, Pan
Chen, Zhensen
Liao, Ying
Chen, Shuo
Guo, Rui
Qi, Haikun
Li, Xuesong
Zhang, Xue
Hu, Zhangxuan
Lu, Hanzhang
Chen, Huijun - Abstract:
- 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 (R 2 ), 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 R 2 and significantly improved ICC for single‐compartment derived bolus arrival time (BAT) and two‐compartment derived cerebral blood flow (CBF) and higher or similar R 2 /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 ofAbstract : 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 (R 2 ), 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 R 2 and significantly improved ICC for single‐compartment derived bolus arrival time (BAT) and two‐compartment derived cerebral blood flow (CBF) and higher or similar R 2 /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. … (more)
- Is Part Of:
- Magnetic resonance in medicine. Volume 84:Issue 2(2020)
- Journal:
- Magnetic resonance in medicine
- Issue:
- Volume 84:Issue 2(2020)
- Issue Display:
- Volume 84, Issue 2 (2020)
- Year:
- 2020
- Volume:
- 84
- Issue:
- 2
- Issue Sort Value:
- 2020-0084-0002-0000
- Page Start:
- 1024
- Page End:
- 1034
- Publication Date:
- 2020-02-04
- Subjects:
- deep learning -- DeepMARS -- MRF‐ASL -- reconstruction -- reproducibility
Nuclear magnetic resonance -- Periodicals
Electron paramagnetic resonance -- Periodicals
616.07548 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1522-2594 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/mrm.28166 ↗
- Languages:
- English
- ISSNs:
- 0740-3194
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5337.798000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 18710.xml