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 pathophysiolog...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.展开更多
基金National Natural Science Foundation of China(62105255,62275210,62375210)National Young Talent Program+3 种基金Key Research and Development Program of Shaanxi(2023-YBSF-293)Shaanxi Young Top-notch Talent Program,Xidian University Specially Funded Project for Interdisciplinary Exploration(TZJH2024043)Fundamental Research Funds for the Central Universities(QTZX24079)Xi'an Science and Technology Project(23ZDCYJSGG0026-2023)。
文摘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.
