Enhancing Contrast in Circular-view Photoacoustic Computed Tomography Systems

In this study, we propose a novel method to enhance image quality in sparse circular-view photoacoustic computed tomography (PACT) systems. Traditional PACT systems rely on dense arrays of detectors to achieve high-resolution images, but they suffer from high cost and computational complexity. Our proposed method addresses these challenges by reconstructing multiple low-quality images (LQIs) from under-sampled data and using advanced signal processing techniques, including cross-correlation and the coefficient of variation (CV), to generate a stability ratio (SR) that significantly improves image quality and contrast. We evaluated the proposed method using both artery and Derenzo phantoms, with detector arrays of 1024, 512, and 256 elements. The results demonstrated that our approach effectively suppresses artifacts and enhances image quality even with a reduced number of detectors. Quantitatively, the proposed method showed substantial improvements over conventional techniques, with up to 50% reduction in background noise, a 6 dB increase in signal-to-noise ratio (SNR), and a significant enhancement in structural similarity index (SSIM). These findings suggest that the proposed method enables high-quality PACT imaging with fewer detectors, which is essential to develop a cost-effective and efficient biomedical imaging tool.