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Optimization of a random linear ultrasonic therapeutic array based on a genetic algorithm.

Honghui Xue¹, Xin Zhang¹, Xiasheng Guo¹

¹Key Laboratory of Modern Acoustics (MOE), School of Physics, Collaborative Innovation Center of Advanced Microstructure, Nanjing University, Nanjing 210093, China.

|May 5, 2022

Summary

This summary is machine-generated.

This study optimized random linear arrays for ultrasonic treatment using a genetic algorithm, significantly improving grating lobe suppression and reducing computation time. The findings provide key parameters for enhanced array design in medical applications.

Keywords:

Genetic algorithm Grating lobe suppression Random linear ultrasonic array Ultrasonic therapy

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Area of Science:

Acoustics and Ultrasonics
Array Signal Processing
Medical Imaging and Therapy

Background:

Randomly arranged linear arrays offer advantages in suppressing grating lobes for ultrasonic treatment.
Optimizing element arrangement is crucial for enhancing array performance and reducing computational load.

Purpose of the Study:

To develop a genetic algorithm-based method for optimizing the random arrangement of linear array elements.
To significantly improve the suppression of grating lobes while reducing calculation time.
To investigate the influence of element kerf, area ratios, and focal distance on grating lobe suppression.

Main Methods:

Utilized a genetic algorithm to optimize the random arrangement of array elements.
Employed maximum and average kerfs as genes and the ratio of maximum to secondary maximum sound pressure as the optimization target.
Reduced calculation requirements to approximately 25% compared to traditional traversing methods.

Main Results:

Demonstrated significant improvement in grating lobe suppression with reduced computational cost.
Identified optimal ranges for element width ratios (5-8), effective element area ratios (0.5-0.9), and maximizing the effective emission aperture.
Experimental validation confirmed that measured sound field results align with simulation predictions.

Conclusions:

The proposed genetic algorithm method effectively optimizes random linear arrays for enhanced grating lobe suppression in ultrasonic applications.
Key design parameters, including element geometry and aperture ratios, are critical for achieving optimal performance.
The optimized arrays show excellent agreement between simulated and experimental results, validating the method's efficacy.