Enhancing image reconstruction in photoacoustic imaging using spatial coherence mean-to-standard-deviation factor...

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Acoustics

Background:

• Photoacoustic imaging (PAI) relies on delay-and-sum (DAS) beamforming for image reconstruction.
• DAS beamforming suffers from high sidelobe artifacts and low contrast, limiting structural differentiation.
• Improving image quality in PAI is crucial for accurate diagnosis and research.

Purpose of the Study:

• To develop an advanced reconstruction algorithm for PAI that overcomes the limitations of traditional DAS.
• To introduce an adaptive weighting factor, the spatial coherence mean-to-standard deviation factor (scMSF), to enhance image quality.
• To evaluate the effectiveness of the scMSF method in improving contrast, resolution, and target detectability in PAI.

Main Methods:

• An adaptive weighting factor, scMSF, was developed and extended into the spatial frequency domain.
• The scMSF was integrated with the minimum variance (MV) algorithm to reduce clutter and enhance contrast.
• Quantitative phantom and in-vivo experiments were conducted to assess the performance of the proposed method.

Main Results:

• Phantom experiments showed improvements in contrast ratio (CR) by 30.15 dB and signal-to-noise ratio (SNR) by 8.62 dB compared to DAS.
• The full-width at half maxima (FWHM) was improved by 56% with the scMSF method.
• In-vivo results demonstrated a 25.6% enhancement in generalized contrast-to-noise ratio (gCNR) over DAS and a 22.5% improvement over MV, indicating better target detectability.

Conclusions:

• The proposed scMSF-based reconstruction method significantly enhances image quality in PAI.
• This technique effectively reduces artifacts and improves contrast and resolution, leading to superior target detectability.
• The scMSF algorithm offers a promising advancement for PAI applications requiring high-fidelity imaging.