Quantitative photoacoustic imaging algorithm using sparse decomposition for photoacoustic and ultrasound dual-mode imaging

In quantitative photoacoustic tomography (qPAT), raw photoacoustic (PA) images offer only an indirect representation of the structural and physiological details of biological tissues. To address this issue, precise absorption coefficient (AC) extraction algorithms are essential for converting PA images into accurate AC maps, which involves mitigating the effects of non-uniform light fluence (LF). This study employs a dual-modality approach using photoacoustic tomography and ultrasound (PAUS), where ultrasonic segmentation identifies key structural boundaries, guiding the construction of Monte Carlo (MC) optical transport models. Leveraging sparse signal representation theory, we introduce a novel quantitative reconstruction algorithm that efficiently separates LF components from PA signals, refining AC imaging accuracy in complex tissues. Through numerical simulations and experiments with tissue-mimicking phantoms and in vivo mouse models using a PAUS system, we demonstrate our algorithm's capability. Results indicate significant improvements in feature visibility, boundary definition, and overall image quality, along with enhanced structural and functional information. This work aims to advance the detection and quantitative imaging of blood vessels and organs.