Multiple-Focus Patterns of Sparse Random Array Using Particle Swarm Optimization for Ultrasound Surgery.

Area of Science:

• Acoustics and Biomedical Engineering
• Medical Physics and Ultrasound Technology

Background:

• Focused ultrasound (FUS) offers non-invasive therapeutic potential for brain tumors, blood-brain barrier (BBB) opening, and neuromodulation.
• Traditional transducer arrays face limitations in scanning range and focal pattern flexibility, hindering treatment of large or complex targets.

Purpose of the Study:

• To investigate the feasibility of using sparse random arrays driven by particle swarm optimization (PSO) for advanced FUS applications.
• To demonstrate the generation of multiple-focus patterns and extended scanning ranges without grating lobes for enhanced FUS treatments.

Main Methods:

• Simulations were performed using sparse random spherical arrays (200 and 660 elements) operating at 1.1 MHz.
• Particle swarm optimization (PSO) algorithm was employed to drive the arrays and generate desired focus patterns.
• Key parameters such as scanning range, focal volume, and grating lobe presence were analyzed for different array configurations.

Main Results:

• A 200-element random array achieved double the off-axis scanning range compared to ordinary symmetric arrays.
• Multiple-focus patterns generated by the random array resulted in an 18-fold increase in focal volume.
• A 660-element sparse random array demonstrated steering up to ±23 mm radially without grating lobes, with 'S'-shaped deflections reaching 25 mm.

Conclusions:

• Combining PSO with sparse random arrays provides a feasible method for generating flexible, 3D-steerable FUS beams.
• This approach significantly enhances focal volume and scanning capabilities, offering potential for shorter treatment times for large targets.
• The developed technique shows promise for adaptable FUS treatments in various neurological and oncological applications.