A dedicated eight-channel receive RF coil array for monkey brain MRI at 9.4 T.


Journal

NMR in biomedicine
ISSN: 1099-1492
Titre abrégé: NMR Biomed
Pays: England
ID NLM: 8915233

Informations de publication

Date de publication:
10 2020
Historique:
received: 15 10 2018
revised: 09 06 2020
accepted: 15 06 2020
pubmed: 31 7 2020
medline: 29 9 2021
entrez: 31 7 2020
Statut: ppublish

Résumé

The neuroimaging of nonhuman primates (NHPs) realised with magnetic resonance imaging (MRI) plays an important role in understanding brain structures and functions, as well as neurodegenerative diseases and pathological disorders. Theoretically, an ultrahigh field MRI (≥7 T) is capable of providing a higher signal-to-noise ratio (SNR) for better resolution; however, the lack of appropriate radiofrequency (RF) coils for 9.4 T monkey MRI undermines the benefits provided by a higher field strength. In particular, the standard volume birdcage coil at 9.4 T generates typical destructive interferences in the periphery of the brain, which reduces the SNR in the neuroscience-focused cortex region. Also, the standard birdcage coil is not capable of performing parallel imaging. Consequently, extended scan durations may cause unnecessary damage due to overlong anaesthesia. In this work, assisted by numerical simulations, an eight-channel receive RF coil array was specially designed and manufactured for imaging NHPs at 9.4 T. The structure and geometry of the proposed receive array was optimised with numerical simulations, so that the SNR enhancement region was particularly focused on monkey brain. Validated with rhesus monkey and cynomolgus monkey brain images acquired from a 9.4 T MRI scanner, the proposed receive array outperformed standard birdcage coil with higher SNR, mean diffusivity and fractional anisotropy values, as well as providing better capability for parallel imaging.

Identifiants

pubmed: 32729642
doi: 10.1002/nbm.4369
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e4369

Informations de copyright

© 2020 John Wiley & Sons, Ltd.

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Auteurs

Mingyan Li (M)

School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.

Yu Li (Y)

School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.

Jin Jin (J)

School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.
Siemens Healthcare Pty. Ltd., Bowen Hills QLD, 4006, Australia.

Zhengyi Yang (Z)

School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

Baogui Zhang (B)

Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

Yanyan Liu (Y)

Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

Ming Song (M)

Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
University of Chinese Academy of Sciences, Beijing, China.

Craig Freakly (C)

School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.

Ewald Weber (E)

School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.

Feng Liu (F)

School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.

Tianzi Jiang (T)

Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
University of Chinese Academy of Sciences, Beijing, China.
CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia.

Stuart Crozier (S)

School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.

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