A Roadmap to Holographic Focused Ultrasound Approaches for Generating Gradient Thermal Patterns.
Journal:
International journal for numerical methods in biomedical engineering
Published Date:
Jun 1, 2025
Abstract
In therapeutic focused ultrasound (FUS), such as thermal ablation and hyperthermia, effective acousto-thermal manipulation requires precise targeting of complex geometries, sound wave propagation through irregular structures, and selective focusing at specific depths. Acoustic holographic lenses (AHLs) provide a distinctive capability to shape acoustic fields into precise, complex, and multifocal FUS-thermal patterns. Acknowledging the under-explored potential of AHLs in shaping ultrasound-induced heating patterns, this study introduces a roadmap for acousto-thermal modeling in the design of AHLs. Three primary modeling approaches are studied and contrasted using four distinct shape groups for the imposed target field. They include pressure-based time reversal (TR) (basic (BSC-TR) and iterative (ITER-TR)), temperature-based (inverse heat transfer optimization (IHTO-TR)), and machine learning (ML)-based (generative adversarial network (GaN) and GaN with feature (Feat-GAN)) methods. Novel metrics, including image quality, thermal efficiency, thermal control, and computational time, are introduced, providing each method's strengths and weaknesses. The importance of evaluating target pattern complexity, thermal and pressure requirements, and computational resources is highlighted. As a further step, two case studies: (1) transcranial FUS and (2) liver hyperthermia, demonstrate the practical use of acoustic holography in therapeutic settings. This paper offers a practical reference for selecting modeling approaches based on therapeutic goals and modeling requirements. Alongside established methods like BSC-TR and ITER-TR, new techniques IHTO-TR, GaN, and Feat-GaN are introduced. BSC-TR serves as a baseline, while ITER-TR enables refinement based on target shape characteristics. IHTO-TR supports thermal control, GaN offers rapid solutions under fixed conditions, and Feat-GaN provides adaptability across varying application settings.
