Enhancing limited-view photoacoustic vascular images via non-linear Lucy-Richardson deconvolution

Photoacoustic microscopy (PAM) is often implemented with a reduced number of sensors to minimize system complexity and cost. However, this results in sparse data acquisition and limited-view detection, which introduces severe artifacts and degrades image quality, consequently hindering the accurate visualization of fine vascular structures. In this study, we propose to use a hybrid image reconstruction framework that combines a nonlinear Lucy-Richardson deconvolution algorithm with an adaptive Kalman filter to enhance vascular detail under limited-view conditions. The proposed method is evaluated using an optical-resolution PAM dataset, demonstrating improved reconstruction fidelity and diagnostic accuracy for both preclinical and clinical imaging applications. Additionally, we benchmark our approach against a recently developed Swin Transformer-based image restoration model to assess its effectiveness in enhancing image clarity and vascular structure delineation. Compared to noisy PAM vessel images, the proposed method achieves a reduction of up to 95% in mean squared error (MSE) and nearly a 97% improvement in structural similarity index measure (SSIM).