Enhancing 3-D Radio Frequency Data in Quantitative Acoustic Microscopy Using Quantum-Driven Prior at 250 MHz and 500 MHz

Quantitative acoustic microscopy (QAM) uses ultrahigh-frequency ultrasound (>200 MHz) to create 2-D maps of acoustic and mechanical properties of tissue at microscopic resolutions ( $\lt 8 ~\mu $ m). Despite significant advancements in QAM, the spatial resolution of current systems, operating at 250 and 500 MHz, may remain insufficient for certain biomedical applications. However, developing a QAM system with finer resolution by using higher-frequency transducers is costly and necessitates skilled operators, and these systems are more sensitive to the outside environment (e.g., vibrations and temperature). This study extends a resolution enhancement framework by proposing a generalized 3-D approach for processing QAM radio frequency (RF) data. The framework utilizes a quantum-based adaptive denoiser, DeQuIP, implemented as a regularization-prior (RED-prior) to enhance QAM maps. Key contributions include temporal hyperparameter optimization, accelerated algorithm integration, and application of quantum interaction theory. DeQuIP employs quantum wave functions, derived from the acquired data, as adaptive transformations that function as an RED-prior. This enables the framework to generate a temporally tailored regularization functional, allowing accurate modeling of complex physical phenomena in ultrasound propagation and providing a significant advantage over traditional regularizations in QAM imaging. The effectiveness of the proposed framework in enhancing resolution is demonstrated through both qualitative and quantitative analyses of experimental 2-D parameter maps obtained from 250- and 500-MHz QAM systems, alongside comparisons with a standard framework. Our framework demonstrates superior performance in recovering fine and subtle details, enhancing the spatial resolution of QAM maps by 38.2%-39.5%, surpassing the state-of-the-art framework, which achieved only 13.4%-26.1% improvement, and shows notable visual improvements in spatial details when compared to histology images.