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Bayesian optimization of gradient trajectory for parallel-transmit pulse design.

Accepted version
Peer-reviewed

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Abstract

PURPOSE: Spoke pulses improve excitation homogeneity in parallel-transmit MRI. We propose an efficient global optimization algorithm, Bayesian optimization of gradient trajectory (BOGAT), for single-slice and simultaneous multislice imaging. THEORY AND METHODS: BOGAT adds an outer loop to optimize kT-space positions. For each position, the RF coefficients are optimized (e.g., with magnitude least squares) and the cost function evaluated. Bayesian optimization progressively estimates the cost function. It automatically chooses the kT-space positions to sample, to achieve fast convergence, often coming close to the globally optimal spoke positions. We investigated the typical features of spokes cost functions by a grid search with field maps comprising 85 slabs from 14 volunteers. We tested BOGAT in this database, and prospectively in a phantom and in vivo. We compared the vendor-provided Fourier transform approach with the same magnitude least squares RF optimizer. RESULTS: The cost function is nonconvex and seen empirically to be piecewise smooth with discontinuities where the underlying RF optimum changes sharply. BOGAT converged to within 10% of the global minimum cost within 30 iterations in 93% of slices in our database. BOGAT achieved up to 56% lower flip angle RMS error (RMSE) or 55% lower pulse energy in phantoms versus the Fourier transform approach, and up to 30% lower RMSE and 29% lower energy in vivo with 7.8 s extra computation. CONCLUSION: BOGAT efficiently estimated near-global optimum spoke positions for the two-spoke tests, reducing flip-angle RMSE and/or pulse energy in a computation time (˜10 s), which is suitable for online optimization.

Description

Keywords

7 T, Bayesian optimization, parallel transmit, pulse design, spokes, ultrahigh field, Humans, Bayes Theorem, Magnetic Resonance Imaging, Algorithms, Phantoms, Imaging, Least-Squares Analysis, Brain

Journal Title

Magn Reson Med

Conference Name

Journal ISSN

0740-3194
1522-2594

Volume Title

Publisher

Wiley
Sponsorship
Medical Research Council (2588937)
Medical Research Council (MR/M008983/1)
EPSRC (EP/T022159/1)
National Institute for Health and Care Research (IS-BRC-1215-20014)
MRC (MR/N013433/1)
M.Z. is supported by the Medical Research Council (MR N013433-1) and the Cambridge Trust. C.T.R. acknowledges research support from Siemens. This research was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The Cambridge 7T MRI facility was co-funded by the University of Cambridge and the Medical Research Council (MR/M008983/1). This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/T022159/1), and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk). We thank Saskia Frisby and Marta Correia for contributing field maps from their pTx scans to the database of scans. We thank Sydney Williams, Samantha Ma, Belinda Ding, Alexander Beckett and David Feinberg for facilitating a remote test of the BOGAT pulses on the IMPULSE 7T Terra scanner at UC Berkeley. BOGAT is the subject of a UK patent application.