期刊
NEUROIMAGE
卷 195, 期 -, 页码 373-383出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2019.03.060
关键词
Quantitative susceptibility mapping; Dipole inversion; Ill-posed problem; Deep learning
资金
- Aalborg University Internationalisation foundation
- Otto Monsted foundation
- Knud Hojgaard Foundation
- Danish Tennis Foundation
- Nordea Foundation
- Julie Damms study-foundation
- Oticon foundation
- Austrian Science Fund (FWF) [KLI523, P30134]
- UQ Postdoctoral Research Fellowship
- Nvidia hardware grant
- Australian Research Council [FT140100865]
- Australian Government
- Australian Research Council [FT140100865] Funding Source: Australian Research Council
- Austrian Science Fund (FWF) [P30134, KLI523] Funding Source: Austrian Science Fund (FWF)
Quantitative susceptibility mapping (QSM) is based on magnetic resonance imaging (MRI) phase measurements and has gained broad interest because it yields relevant information on biological tissue properties, predominantly myelin, iron and calcium in vivo. Thereby, QSM can also reveal pathological changes of these key components in widespread diseases such as Parkinson's disease, Multiple Sclerosis, or hepatic iron overload. While the ill-posed field-to-source-inversion problem underlying QSM is conventionally assessed by the means of regularization techniques, we trained a fully convolutional deep neural network - DeepQSM - to directly invert the magnetic dipole kernel convolution. DeepQSM learned the physical forward problem using purely synthetic data and is capable of solving the ill-posed field-to-source inversion on in vivo MRI phase data. The magnetic susceptibility maps reconstructed by DeepQSM enable identification of deep brain substructures and provide information on their respective magnetic tissue properties. In summary, DeepQSM can invert the magnetic dipole kernel convolution and delivers robust solutions to this ill-posed problem.
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