Genetic algorithm‐based optimization of pulse sequences

Somai, Vencel; Kreis, Felix; Gaunt, Adam; Tsyben, Anastasia; Chia, Ming Li; Hesse, Friederike; Wright, Alan J.; Brindle, Kevin M.

doi:10.1002/mrm.29110

Cited by 6 publications

(4 citation statements)

References 65 publications

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“…After eliminating the characteristic function, the batch gradient descent method can obtain the optimal solution of more data with fewer samples and determine which gradient descent method to choose according to the actual demand (brindle k m et al 2021) [20]. During data analysis, some original data have problems such as duplication or deletion.…”

Section: Prediction Of Urban Scale Expansion and Construction Ofmentioning

confidence: 99%

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Kuang

2022

Journal of Function Spaces

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With the continuous development of urbanization, the urban population is becoming more and more dense, and the demand for land is becoming more and more tense. Urban expansion has become an indispensable part of urban development. This paper studies the optimization of neural network structure by genetic algorithm, puts forward the prediction model of urban scale expansion based on a genetic algorithm optimization neural network, and compares the performance of the model with the basic model. A genetic algorithm BP neural network (GA-BP) optimized by the genetic algorithm is used to shorten the running time of the algorithm and improve the prediction accuracy, but it is easy to fall into local solution. The genetic algorithm is improved by immune cloning algorithm, and the CGA-BP neural network model is established to obtain the global optimal solution. Compared with the BP neural network model and GA-BP neural network model, the CGA-BP neural network model converges faster, and the training times reach the error condition after 79 times, while the BP neural network model and GA-BP neural network model need 117 times and 100 times, respectively, and the fitness value corresponding to the number of iterations of the model is larger. Therefore, the CGA-BP neural network algorithm can make prediction more accurately and quickly and predict the expansion of urban scale through urban conditions.

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Section: Prediction Of Urban Scale Expansion and Construction Ofmentioning

confidence: 99%

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Kuang

2022

Journal of Function Spaces

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“…Proton to nitrogen polarization transfer was as high as 160% more effective when optimized vs. unoptimized BINEPT sequences were compared. The registration of J-coupling artefact-free images was also demonstrated [40].…”

Section: Genetic Algorithms In Mri Pulse Sequence Developmentmentioning

confidence: 96%

Artificial Intelligence-Powered Pulse Sequences in Nuclear Magnetic Resonance and Magnetic Resonance Imaging: Historical Trends, Current Innovations and Perspectives

Tokarz

2024

Sci. Rad.

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This review article explores the historical background and recent advances in the application of artificial intelligence (AI) in the development of radiofrequency pulses and pulse sequences in nuclear magnetic resonance spectroscopy (NMR) and imaging (MRI). The introduction of AI into this field, which traces back to the late 1970s, has recently witnessed remarkable progress, leading to the design of specialized frameworks and software solutions such as DeepRF, MRzero, and GENETICS-AI. Through an analysis of literature and case studies, this review tracks the transformation of AI-driven pulse design from initial proof-of-concept studies to comprehensive scientific programs, shedding light on the potential implications for the broader NMR and MRI communities. The fusion of artificial intelligence and magnetic resonance pulse design stands as a promising frontier in spectroscopy and imaging, offering innovative enhancements in data acquisition, analysis, and interpretation across diverse scientific domains.

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“…While composite pulses are developed with rational input, optimization algorithms such as genetic algorithms [ 23,24 ] and Optimal Control methods (OC) [ 25–27 ] have been shown to allow the design of NMR pulses with rather specific characteristics and/or robust performance. Among others, optimal control theory has been employed for the design of selective and broadband inversion pulses.…”

Section: Introductionmentioning

confidence: 99%

“…Although they have large enough bandwidths for 1 H NMR, they may not be suitable for nuclei that present larger isotropic chemical shift δ iso dispersion or quadrupolar interactions such as 19 F and 11 B, respectively. While composite pulses are developed with rational input, optimization algorithms such as genetic algorithms [23,24] and Optimal Control methods (OC) [25][26][27] have been shown to allow the design of NMR pulses with rather specific characteristics and/or robust performance. Among others, optimal control theory has been employed for the design of selective and broadband inversion pulses.…”

mentioning

confidence: 99%

Echo pulses for background suppression by optimal control

Sanchez

Günne

2023

Magnetic Reson in Chemistry

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Materials used to construct magic‐angle‐spinning NMR probes can contain NMR active nuclei that produce a significant amount of background signal. Because these materials are located outside the sample coil, the use of spatially selective pulses to remove the background is a popular approach for background suppression. However, previously suggested spatially selective pulses suffer from limited excitation bandwidths, which may make them unsuitable for the acquisition of nuclei with a large chemical shift range. Here, a pulse (OC‐BACK) is presented, which has been developed by optimal control, which has a flat profile of ~120 kHz with respect to off‐resonance effects and extended pass and suppression bands with respect to the nominal nutation frequency. The presented solution is large enough to be effective for background suppression schemes in 19F magic‐angle‐spinning NMR at medium and low magnetic fields.

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Genetic algorithm‐based optimization of pulse sequences

Cited by 6 publications

References 65 publications

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Artificial Intelligence-Powered Pulse Sequences in Nuclear Magnetic Resonance and Magnetic Resonance Imaging: Historical Trends, Current Innovations and Perspectives

Echo pulses for background suppression by optimal control

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