Abstract
Acoustic resolution photoacoustic microscopy (AR-PAM) is a non-invasive medical imaging modality that can be employed for deep bio-tissue and vasculature imaging. However, to achieve clinical translation, system improvements and algorithmic techniques are still required to further enhance the imaging performance. In this paper, the hardware modules of the system are specifically analyzed, then the typical AR-PAM system and its miniaturized counterparts have also been demonstrated. Furthermore, optical and acoustic simulation have been proposed to characterize the imaging system’s sensitivity, meanwhile the degradation model of the imaging results has also been obtained with mathematical derivation. With the acquired degradation model, both learning based and model based algorithms are proposed to enhance the raw AR-PAM imaging results. The enhancement results for synthetic and in vivo AR-PAM images have proved the resolution has been enhanced more than 10 times, which exceed the physical resolution limit of AR-PAM modality and achieved super acoustic resolution imaging.
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