A gradient optimization toolbox for general purpose time-optimal MRI gradient waveform design.

gradient waveform design magnetic resonance imaging open source software optimization

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:
12 2020
Historique:
received: 23 03 2020
revised: 22 05 2020
accepted: 26 05 2020
entrez: 19 1 2021
pubmed: 20 1 2021
medline: 15 5 2021
Statut: ppublish

Résumé

To introduce and demonstrate a software library for time-optimal gradient waveform optimization with a wide range of applications. The software enables direct on-the-fly gradient waveform design on the scanner hardware for multiple vendors. The open-source gradient optimization (GrOpt) toolbox was implemented in C with both Matlab and Python wrappers. The toolbox enables gradient waveforms to be generated based on a set of constraints that define the features and encodings for a given acquisition. The GrOpt optimization routine is based on the alternating direction method of multipliers (ADMM). Additional constraints enable error corrections to be added, or patient comfort and safety to be adressed. A range of applications and compute speed metrics are analyzed. Finally, the method is implemented and tested on scanners from different vendors. Time-optimal gradient waveforms for different pulse sequences and the constraints that define them are shown. Additionally, the ability to add, arbitrary motion (gradient moment) compensation or limit peripheral nerve stimulation is demonstrated. There exists a trade-off between computation time and gradient raster time, but it was observed that acceptable gradient waveforms could be generated in 1-40 ms. Gradient waveforms generated and run on the different scanners were functionally equivalent, and the images were comparable. GrOpt is an open source toolbox that enables on-the-fly optimization of gradient waveform design, subject to a set of defined constraints. GrOpt was presented for a range of imaging applications, analyzed in terms of computational complexity, and implemented to run on the scanner for a multi-vendor demonstration.

Identifiants

pubmed: 33463724
doi: 10.1002/mrm.28384
pmc: PMC7540314
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

3234-3245

Informations de copyright

© 2020 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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Auteurs

Michael Loecher (M)

Department of Radiology, Stanford University, Stanford, CA, USA.
Department of Radiology, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.

Matthew J Middione (MJ)

Department of Radiology, Stanford University, Stanford, CA, USA.
Department of Radiology, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.

Daniel B Ennis (DB)

Department of Radiology, Stanford University, Stanford, CA, USA.
Department of Radiology, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.

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