Deep learning-assisted model-based off-resonance correction for non-Cartesian SWI.
3D SPARKLING
deep learning
non-Cartesian imaging
off-resonance correction
unrolled neural network
Journal
Magnetic resonance in medicine
ISSN: 1522-2594
Titre abrégé: Magn Reson Med
Pays: United States
ID NLM: 8505245
Informations de publication
Date de publication:
10 2023
10 2023
Historique:
revised:
17
04
2023
received:
29
11
2022
accepted:
15
05
2023
medline:
31
7
2023
pubmed:
22
6
2023
entrez:
22
6
2023
Statut:
ppublish
Résumé
Patient-induced inhomogeneities in the static magnetic field cause distortions and blurring (off-resonance artifacts) during acquisitions with long readouts such as in SWI. Conventional versatile correction methods based on extended Fourier models are too slow for clinical practice in computationally demanding cases such as 3D high-resolution non-Cartesian multi-coil acquisitions. Most reconstruction methods can be accelerated when performing off-resonance correction by reducing the number of iterations, compressed coils, and correction components. Recent state-of-the-art unrolled deep learning architectures could help but are generally not adapted to corrupted measurements as they rely on the standard Fourier operator in the data consistency term. The combination of correction models and neural networks is therefore necessary to reduce reconstruction times. Hybrid pipelines using UNets were trained stack-by-stack over 99 SWI 3D SPARKLING 20-fold accelerated acquisitions at 0.6 mm isotropic resolution using different off-resonance correction methods. Target images were obtained using slow model-based corrections based on self-estimated The proposed hybrid pipelines achieved scores competing with two to three times slower baseline methods, and neural networks were observed to contribute both as pre-conditioner and through inter-iteration memory by allowing more degrees of freedom over the model design. A combination of model-based and network-based off-resonance correction was proposed to significantly accelerate conventional methods. Different promising synergies were observed between acceleration factors (iterations, coils, correction) and model/network that could be expanded in the future.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1431-1445Informations de copyright
© 2023 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.
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