Published December 6, 2021 | Version v1
Journal article Open

Genetic algorithm-based optimization of pulse sequences

  • 1. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
  • 2. Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
  • 3. Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom


Supplementary Matlab code and raw data for the paper: Somai V, Kreis F, Gaunt A, et al. Genetic algorithm- based optimization of pulse sequences. Magn Reson Med. 2021;00:1– 15. doi:10.1002/mrm.29110

Short summary of the paper: The performance of pulse sequences in vivo can be limited by fast re-laxation 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 imple-menting arbitrarily complex cost functions. Efficient methods for simulating spin dynamics and incorporating frequency selectivity are also described and tested in heteronuclear magnetic resonance experiments.



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AlternativesToGd – Hyperpolarised MR technologies and molecular probes as alternatives for conventional metal-containing contrast agents for MRI examinations 858149
European Commission