Acoustic holographic lenses (AHLs) show great potential for sound
manipulation. These lenses store the phase and amplitude profile of the desired
wavefront when illuminated by a single acoustic source to reconstruct focused
ultrasound (FUS) pressure fields, induce localized heating, and achieve
temporal and spatial thermal effects in acousto-thermal materials like
polymers. The ultrasonic energy is transmitted and focused by AHL from a
transducer into a particular focal volume. It is then converted to heat by
internal friction in the polymer chains, causing the temperature of the polymer
to rise at the focus locations while having little to no effect elsewhere. This
one-of-a-kind capability is made possible by the development of AHLs to make
use of the translation of attenuated pressure fields into programmable heat
patterns. However, the acousto-thermal dynamics of AHLs are largely unexplored.
We use a machine learning-assisted single inverse problem approach for rapid
and efficient AHL designs. The process involves the conversion of thermal
information into a holographic representation through the utilization of two
latent functions; pressure phase and amplitude. Experimental verification is
performed for pressure and thermal measurements. The volumetric acousto-thermal
analysis of experimental samples is performed to offer knowledge of the
obtained pattern dynamics, as well as the applicability of holographic FUS
thermal mapping for precise temperature control in complex volumes of
heterogeneous media. The proposed framework provides a solid foundation for
anticipating and assessing thermal changes within materials using only outer
surface measurements since it can correlate with surface temperature data
alone.Comment: 13 pages, 4 figures, 1 tabl