A Quantum Denoising-Based Resolution Enhancement Framework for 250-MHz and 500-MHz Quantitative Acoustic Microscopy.

Area of Science:

• Biomedical Imaging
• Quantum Computing Applications
• Acoustic Microscopy

Background:

• Quantitative acoustic microscopy (QAM) provides 2D maps of tissue acoustic properties at microscopic scales (<8µm).
• Current QAM systems (250-MHz, 500-MHz) have limitations in spatial resolution for clinical applications.
• Achieving higher resolution via hardware is costly and requires specialized expertise.

Purpose of the Study:

• To develop a computational method for enhancing the spatial resolution of 2D QAM maps.
• To leverage quantum-based denoising for improved image restoration in QAM.
• To overcome the limitations of current QAM hardware for finer clinical imaging.

Main Methods:

• Implemented a super-resolution scheme for 2D QAM by integrating an off-the-shelf quantum-based adaptive denoiser (DeQuIP).
• Utilized regularization-by-denoising (RED) principles, coupling DeQuIP as a RED-prior with an analytical solution.
• Applied the method to experimental 2D acoustic-impedance maps (2DZMs) from 250-MHz and 500-MHz QAM data.

Main Results:

• The proposed scheme significantly improved the spatial resolution of 2DZMs.
• A 40% resolution enhancement was achieved for 250-MHz QAM data, surpassing existing methods (23-32% improvement).
• The method effectively recovered finer details and subtle features in the acoustic impedance maps.

Conclusions:

• The developed RED scheme with a quantum denoiser is effective for enhancing QAM spatial resolution.
• This computational approach offers a cost-effective alternative to hardware upgrades for higher-resolution QAM.
• The findings demonstrate the potential of quantum-inspired methods in biomedical image processing.