A Roadmap to Holographic Focused Ultrasound Approaches to Generate Thermal Patterns

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, 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 (BSC-TR and ITER-TR), temperature-based (IHTO-TR), and machine learning (ML)-based (GaN and Feat-GAN) methods. New metrics including image quality, thermal efficiency, control, and computational time are introduced. The importance of evaluating target pattern complexity, thermal and pressure requirements, and computational resources is highlighted for selecting the appropriate methods. For lightly heterogeneous media and targets with lower pattern complexity, BSC-TR combined with error diffusion algorithms provides an effective solution. As pattern complexity increases, ITER-TR becomes more suitable, enabling optimization through iterative forward and backward propagations controlled by different error metrics. IHTO-TR is recommended for highly heterogeneous media, particularly in applications requiring thermal control and precise heat deposition. GaN is ideal for rapid solutions that account for acousto-thermal effects, especially when model parameters and boundary conditions remain constant. In contrast, Feat-GaN is effective for moderately complex shape groups and applications where model parameters must be adjusted.