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HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting

Song, P; Eldar, YC; Mazor, G; Rodrigues, MRD; (2019) HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting. Medical Physics , 46 (11) pp. 4951-4969. 10.1002/mp.13727. Green open access

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Purpose: Magnetic resonance fingerprinting (MRF) methods typically rely on dictionary matching to map the temporal MRF signals to quantitative tissue parameters. Such approaches suffer from inherent discretization errors, as well as high computational complexity as the dictionary size grows. To alleviate these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting (HYDRA) approach, referred to as HYDRA. / Methods: HYDRA involves two stages: a model‐based signature restoration phase and a learning‐based parameter restoration phase. Signal restoration is implemented using low‐rank based de‐aliasing techniques while parameter restoration is performed using a deep nonlocal residual convolutional neural network. The designed network is trained on synthesized MRF data simulated with the Bloch equations and fast imaging with steady‐state precession (FISP) sequences. In test mode, it takes a temporal MRF signal as input and produces the corresponding tissue parameters. / Results: We validated our approach on both synthetic data and anatomical data generated from a healthy subject. The results demonstrate that, in contrast to conventional dictionary matching‐based MRF techniques, our approach significantly improves inference speed by eliminating the time‐consuming dictionary matching operation, and alleviates discretization errors by outputting continuous‐valued parameters. We further avoid the need to store a large dictionary, thus reducing memory requirements. / Conclusions: Our approach demonstrates advantages in terms of inference speed, accuracy, and storage requirements over competing MRF methods.

Type: Article
Title: HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/mp.13727
Publisher version: https://doi.org/10.1002/mp.13727
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Magnetic Resonance Fingerprinting, Quantitative Magnetic Resonance Imaging, Deep Learning, Nonlocal Residual Convolutional Neural Network, Self attention
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10081158
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