Repository logo
 

Genetic algorithm-based optimization of pulse sequences.

Published version
Peer-reviewed

Change log

Authors

Kreis, Felix 
Gaunt, Adam 
Tsyben, Anastasia 
Chia, Ming Li 

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.

Description

Funder: Cambridge Commonwealth, European and International Trust; Id: http://dx.doi.org/10.13039/501100003343

Keywords

MRI, hyperpolarized, metabolism, numerical optimization, pulse sequence, Algorithms, Lactic Acid, Magnetic Resonance Imaging, Phantoms, Imaging, Protons

Journal Title

Magn Reson Med

Conference Name

Journal ISSN

0740-3194
1522-2594

Volume Title

Publisher

Wiley
Sponsorship
Cancer Research UK (C96/A25177)
Cancer Research UK (C55296/A26605)
European Commission Horizon 2020 (H2020) Future and Emerging Technologies (FET) (858149)