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SHR Neuro Krebs Kardio Lipid

Bollmann, S; Rasmussen, KGB; Kristensen, M; Blendal, RG; Østergaard, LR; Plocharski, M; O'Brien, K; Langkammer, C; Janke, A; Barth, M.
DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping.
Neuroimage. 2019; 195(2):373-383
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Autor/innen der Med Uni Graz:
Langkammer Christian
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Abstract:
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. Copyright © 2019 Elsevier Inc. All rights reserved.
Find related publications in this database (using NLM MeSH Indexing)
Adult -
Algorithms -
Brain - physiology
Brain Mapping - methods
Deep Learning -
Female -
Humans -
Image Processing, Computer-Assisted - methods
Magnetic Resonance Imaging - methods
Male -
Young Adult -

Find related publications in this database (Keywords)
Quantitative susceptibility mapping
Dipole inversion
Ill-posed problem
Deep learning
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