Bridging the gap: improving correspondence between low-field and high-field magnetic resonance images in young people.

SynthSR low field MRI magnetic resonance imaging pediatric neuroimaging super-resolution

Journal

Frontiers in neurology
ISSN: 1664-2295
Titre abrégé: Front Neurol
Pays: Switzerland
ID NLM: 101546899

Informations de publication

Date de publication:
2024
Historique:
received: 15 11 2023
accepted: 19 01 2024
medline: 8 4 2024
pubmed: 8 4 2024
entrez: 8 4 2024
Statut: epublish

Résumé

Portable low-field-strength magnetic resonance imaging (MRI) systems represent a promising alternative to traditional high-field-strength systems with the potential to make MR technology available at scale in low-resource settings. However, lower image quality and resolution may limit the research and clinical potential of these devices. We tested two super-resolution methods to enhance image quality in a low-field MR system and compared their correspondence with images acquired from a high-field system in a sample of young people. T1- and T2-weighted structural MR images were obtained from a low-field (64mT) Hyperfine and high-field (3T) Siemens system in Single pairs of T1- and T2-weighted images acquired at low field showed high correspondence to high-field-strength images for estimates of total intracranial volume, surface area cortical volume, subcortical volume, and total brain volume ( Applying super-resolution approaches to low-field imaging improves regional brain volume and surface area accuracy in young people. Finer-scale brain measurements, such as cortical thickness, remain challenging with the limited resolution of low-field systems.

Sections du résumé

Background UNASSIGNED
Portable low-field-strength magnetic resonance imaging (MRI) systems represent a promising alternative to traditional high-field-strength systems with the potential to make MR technology available at scale in low-resource settings. However, lower image quality and resolution may limit the research and clinical potential of these devices. We tested two super-resolution methods to enhance image quality in a low-field MR system and compared their correspondence with images acquired from a high-field system in a sample of young people.
Methods UNASSIGNED
T1- and T2-weighted structural MR images were obtained from a low-field (64mT) Hyperfine and high-field (3T) Siemens system in
Results UNASSIGNED
Single pairs of T1- and T2-weighted images acquired at low field showed high correspondence to high-field-strength images for estimates of total intracranial volume, surface area cortical volume, subcortical volume, and total brain volume (
Conclusion UNASSIGNED
Applying super-resolution approaches to low-field imaging improves regional brain volume and surface area accuracy in young people. Finer-scale brain measurements, such as cortical thickness, remain challenging with the limited resolution of low-field systems.

Identifiants

pubmed: 38585353
doi: 10.3389/fneur.2024.1339223
pmc: PMC10995930
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1339223

Informations de copyright

Copyright © 2024 Cooper, Hayes, Corcoran, Sheth, Arnold, Stein, Glahn and Jalbrzikowski.

Déclaration de conflit d'intérêts

TA is an employee of Subtle Medical. JS has received support through sponsored-research agreements with Hyperfine Research, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Rebecca Cooper (R)

Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, United States.
Department of Psychiatry, Harvard Medical School, Boston, MA, United States.

Rebecca A Hayes (RA)

Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, United States.

Mary Corcoran (M)

Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, United States.

Kevin N Sheth (KN)

Center for Brain and Mind Health, Yale School of Medicine, New Haven, CT, United States.

Thomas Campbell Arnold (TC)

Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States.

Joel M Stein (JM)

Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States.
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

David C Glahn (DC)

Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, United States.
Department of Psychiatry, Harvard Medical School, Boston, MA, United States.
Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, United States.

Maria Jalbrzikowski (M)

Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, United States.
Department of Psychiatry, Harvard Medical School, Boston, MA, United States.

Classifications MeSH