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 fast 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. Despite the potential of non-diffracting beams and sparse image reconstruction, previous efforts have often overlooked the simultaneous optimization of these aspects, leading to compromised imaging quality and limited applicability in clinical settings. Our work bridges this gap by focusing on comprehensive multitask learning of SDAM Wasserstein-based CycleGAN (SW-CycleGAN), ensuring that ML-LR-PAM delivers artifact-minimized images with large-volume, high-resolution capabilities, thus enabling rapid and repeatable monitoring performance. We anticipate that the integration of these software and hardware advancements will facilitate ML-LR-PAM as a standard tool in both biomedical research laboratories and clinical applications.