Photoacoustic imaging of the arteries can provide valuable functional information to diagnose and classify atherosclerotic plaques. When combined with ultrasound for anatomical information, photoacoustic imaging holds promise for routine monitoring and for treatment decisions. However, utilizing conventional ultrasound systems for combined photoacoustic and ultrasound imaging has not been successful for this application. In this study, we imaged two major arteries susceptible to atherosclerosis—the carotid artery and the femoral artery—using a linear array-based system. Our results demonstrate the feasibility of imaging the carotid artery at depths of up to 10 mm and the femoral arteries at up to 20 mm. The average success rates for imaging the carotid, common femoral and superficial femoral arteries in healthy volunteers were 75%, 100% and 33.3%, respectively, demonstrating potential for future studies.

Photoacoustic imaging offers optical contrast images of human tissue at acoustic resolution, making it valuable for diverse clinical applications. However, quantifying tissue composition via optical contrast remains challenging due to the unknown light fluence within the tissue. Here, we propose a method that leverages known chromophores (e.g., arterial blood) to improve the accuracy of quantitative photoacoustic imaging. By using the optical properties of a known chromophore as a fluence marker and integrating it into the optical inversion process, we can estimate the unknown fluence within the tissue. Experimentally, we demonstrate that this approach successfully recovers both the spectral shape and magnitude of the optical absorption coefficient of an unknown chromophore. Additionally, we show that the fluence marker method enhances conventional optical inversion techniques, specifically (i) a straightforward iterative approach and (ii) a gradient-based method. Our results indicate an improvement in accuracy of up to 24.4% when comparing optical absorption recovery with and without the fluence marker. Finally, we present the method's performance and illustrate its applications in carotid plaque quantification.