A Quantum Denoising-Based Red Framework for 250-Mhz & 500-Mhz Quantitative Acoustic-Microscopy Resolution Enhancement

Sayantan Dutta, Jonathan Mamou

Abstract: Quantitative acoustic microscopy (QAM) is an imaging modality to form two-dimensional (2D) quantitative maps of acoustic properties of soft-tissues at a microscopic scale (<8µm). Our custom-made QAM systems employ a 250MHz or a 500-MHz single-element transducer to produce 2D maps with spatial resolutions of 7 µm or 4µm, respectively. These state-of-the-art QAM systems remain inadequate for clinical investigations requiring even finer resolution, are costly to build, and require expert users. In this work, we propose to enhance the spatial resolution of the 2D maps by exploiting an off-the-shelf quantum-based adaptive denoiser (DeQuIP), leveraging the principles of quantum many-body theory. Adapting the recent breakthrough of regularization-by-denoising (RED) in image restoration, we impose this external DeQuIP denoiser as RED-prior combined with an analytical solution to handle the degradation operators for solving QAM super-resolution imaging. The efficiency of our proposed method is demonstrated by improving the resolution of experimental 2D acoustic impedance maps generated from the data acquired by 250-MHz and 500-MHz QAM systems. Results demonstrate that our RED algorithm permits the recovery of finer details and increased resolution gain (RG) by 2.26 to 3.18 when applied to 2D impedance maps. These RG values were also greater than obtained with two other state-of-the-art methods (only 1.67-3.03 RG values).