Genetic algorithm-based optimization of pulse sequences.
Authors
Kreis, Felix
Gaunt, Adam
Tsyben, Anastasia
Chia, Ming Li
Hesse, Friederike
Publication Date
2022-05Journal Title
Magn Reson Med
ISSN
0740-3194
Publisher
Wiley
Language
en
Type
Article
This Version
AO
VoR
Metadata
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Somai, V., Kreis, F., Gaunt, A., Tsyben, A., Chia, M. L., Hesse, F., Wright, A. J., & et al. (2022). Genetic algorithm-based optimization of pulse sequences.. Magn Reson Med https://doi.org/10.1002/mrm.29110
Description
Funder: Cambridge Commonwealth, European and International Trust; Id: http://dx.doi.org/10.13039/501100003343
Abstract
PURPOSE: The performance of pulse sequences in vivo can be limited by fast relaxation rates, magnetic field inhomogeneity, and nonuniform spin excitation. We describe here a method for pulse sequence optimization that uses a stochastic numerical solver that in principle is capable of finding a global optimum. The method provides a simple framework for incorporating any constraint and implementing arbitrarily complex cost functions. Efficient methods for simulating spin dynamics and incorporating frequency selectivity are also described. METHODS: Optimized pulse sequences for polarization transfer between protons and X-nuclei and excitation pulses that eliminate J-coupling modulation were evaluated experimentally using a surface coil on phantoms, and also the detection of hyperpolarized [2-13 C]lactate in vivo in the case of J-coupling modulation-free excitation. RESULTS: The optimized polarization transfer pulses improved the SNR by ~50% with a more than twofold reduction in the B1 field, and J-coupling modulation-free excitation was achieved with a more than threefold reduction in pulse length. CONCLUSION: This process could be used to optimize any pulse when there is a need to improve the uniformity and frequency selectivity of excitation as well as to design new pulses to steer the spin system to any desired achievable state.
Keywords
MRI, hyperpolarized, metabolism, numerical optimization, pulse sequence, Algorithms, Lactic Acid, Magnetic Resonance Imaging, Phantoms, Imaging, Protons
Sponsorship
Cancer Research UK (C96/A25177)
Cancer Research UK (C55296/A26605)
European Commission Horizon 2020 (H2020) Future and Emerging Technologies (FET) (858149)
Identifiers
mrm29110
External DOI: https://doi.org/10.1002/mrm.29110
This record's URL: https://www.repository.cam.ac.uk/handle/1810/331779
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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