研究目的
To propose a new adaptive wavelet threshold de-noising (aWTD) algorithm for improving the signal-to-noise ratio (SNR) of photoacoustic signals without sacrificing signal fidelity and imaging speed.
研究成果
The proposed aWTD algorithm significantly improves PA signal SNR and image contrast compared to existing methods, offering potential for real-time, low-cost PA tomography systems with low-power laser sources.
研究不足
The study focuses on improving SNR in scenarios of low laser power and deep penetration but may not address all types of noise or signal distortions in PA tomography. The experimental setup is specific to certain conditions and may require adaptation for broader applications.
1：Experimental Design and Method Selection:
The study involves the development and application of an adaptive wavelet threshold de-noising (aWTD) algorithm to improve PA signal SNR. The methodology includes wavelet transform for signal decomposition, adaptive threshold selection, and signal reconstruction.
2：Sample Selection and Data Sources:
Numerical simulations using k-Wave toolbox in MATLAB and experimental measurements with a customized photoacoustic tomography system were conducted.
3：List of Experimental Equipment and Materials:
A fiber-coupled pulsed laser, non-focused ultrasound transducer, pulser-receiver (5072PR, Olympus), oscilloscope (DS1204B, Rigol), and a 3D printed phantom were used.
4：Experimental Procedures and Operational Workflow:
PA signals were detected, amplified, and processed using the proposed aWTD algorithm. The performance was compared with other de-noising methods.
5：Data Analysis Methods:
Signal to noise ratio (SNR), mean squared error (MSE), normalized correlation (NCC), and contrast to noise ratio (CNR) were used to assess the de-noising effect and imaging performance.
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