Multitask learning-powered large-volume, rapid photoacoustic microscopy with non-diffracting beams excitation and sparse sampling

Large-volume photoacoustic microscopy (PAM) or rapid PAM has attracted increasing attention in biomedical applications due to its ability to provide detailed structural and functional information on tumor pathophysiology and the neuroimmune microenvironment. Non-diffracting beams, such as Airy beams, offer extended depth-of-field (DoF), while sparse image reconstruction using deep learning enables image recovery for rapid imaging. However, Airy beams often introduce side-lobe artifacts, and achieving both extended DoF and rapid imaging remains a challenge, hindering PAM’s adoption as a routine large-volume and repeatable monitoring tool. To address these challenges, we developed multitask learning-powered large-volume, rapid photoacoustic microscopy with Airy beams (ML-LR-PAM). This approach integrates advanced software and hardware solutions designed to mitigate side-lobe artifacts and achieve super-resolution reconstruction. Unlike previous methods that neglect the simultaneous optimization of these aspects, our approach bridges this gap by employing scaled dot-product attention mechanism (SDAM) Wasserstein-based CycleGAN (SW-CycleGAN) for artifact reduction and high-resolution, large-volume imaging. We anticipate that ML-LR-PAM, through this integration, will become a standard tool in both biomedical research and clinical practice.