Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T.


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:
04 2022
Historique:
revised: 19 10 2021
received: 01 03 2021
accepted: 12 11 2021
pubmed: 11 12 2021
medline: 17 3 2022
entrez: 10 12 2021
Statut: ppublish

Résumé

Fetal brain Magnetic Resonance Imaging suffers from unpredictable and unconstrained fetal motion that causes severe image artifacts even with half-Fourier single-shot fast spin echo (HASTE) readouts. This work presents the implementation of a closed-loop pipeline that automatically detects and reacquires HASTE images that were degraded by fetal motion without any human interaction. A convolutional neural network that performs automatic image quality assessment (IQA) was run on an external GPU-equipped computer that was connected to the internal network of the MRI scanner. The modified HASTE pulse sequence sent each image to the external computer, where the IQA convolutional neural network evaluated it, and then the IQA score was sent back to the sequence. At the end of the HASTE stack, the IQA scores from all the slices were sorted, and only slices with the lowest scores (corresponding to the slices with worst image quality) were reacquired. The closed-loop HASTE acquisition framework was tested on 10 pregnant mothers, for a total of 73 acquisitions of our modified HASTE sequence. The IQA convolutional neural network, which was successfully employed by our modified sequence in real time, achieved an accuracy of 85.2% and area under the receiver operator characteristic of 0.899. The proposed acquisition/reconstruction pipeline was shown to successfully identify and automatically reacquire only the motion degraded fetal brain HASTE slices in the prescribed stack. This minimizes the overall time spent on HASTE acquisitions by avoiding the need to repeat the entire stack if only few slices in the stack are motion-degraded.

Identifiants

pubmed: 34888942
doi: 10.1002/mrm.29106
pmc: PMC8810713
mid: NIHMS1764763
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1914-1922

Subventions

Organisme : NICHD NIH HHS
ID : R00 HD074649
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD099846
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD100009
Pays : United States
Organisme : NICHD NIH HHS
ID : P50 HD105351
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD085813
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB032708
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB017337
Pays : United States
Organisme : NICHD NIH HHS
ID : U01 HD087211
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD093578
Pays : United States
Organisme : NIBIB NIH HHS
ID : P41 EB015902
Pays : United States

Informations de copyright

© 2021 International Society for Magnetic Resonance in Medicine.

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Auteurs

Borjan Gagoski (B)

Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA.
Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.

Junshen Xu (J)

Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

Paul Wighton (P)

Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.

M Dylan Tisdall (MD)

Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Robert Frost (R)

Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.

Wei-Ching Lo (WC)

Siemens Medical Solutions USA, Inc, Charlestown, Massachusetts, USA.

Polina Golland (P)

Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

Andre van der Kouwe (A)

Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.

Elfar Adalsteinsson (E)

Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

P Ellen Grant (PE)

Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA.
Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.

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