Despite all the expectations for photoacoustic endoscopy (PAE), there are still several technical issues that must be resolved before the technique can be successfully translated into clinics. Among these, electromagnetic interference (EMI) noise, in addition to the limited signal-to-noise ratio (SNR), have hindered the rapid development of related technologies. Unlike endoscopic ultrasound, in which the SNR can be increased by simply applying a higher pulsing voltage, there is a fundamental limitation in leveraging the SNR of PAE signals because they are mostly determined by the optical pulse energy applied, which must be within the safety limits. Moreover, a typical PAE hardware situation requires a wide separation between the ultrasonic sensor and the amplifier, meaning that it is not easy to build an ideal PAE system that would be unaffected by EMI noise. With the intention of expediting the progress of related research, in this study, we investigated the feasibility of deep-learning-based EMI noise removal involved in PAE image processing. In particular, we selected four fully convolutional neural network architectures, U-Net, Segnet, FCN-16s, and FCN-8s, and observed that a modified U-Net architecture outperformed the other architectures in the EMI noise removal. Classical filter methods were also compared to confirm the superiority of the deep-learning-based approach. Still, it was by the U-Net architecture that we were able to successfully produce a denoised 3D vasculature map that could even depict the mesh-like capillary networks distributed in the wall of a rat colorectum. As the development of a low-cost laser diode or LED-based photoacoustic tomography (PAT) system is now emerging as one of the important topics in PAT, we expect that the presented AI strategy for the removal of EMI noise could be broadly applicable to many areas of PAT, in which the ability to apply a hardware-based prevention method is limited and thus EMI noise appears more prominently due to poor SNR.

Background/Objectives: Traditional thermal ablation for pancreatic cancer is limited by collateral injury, often leading to complications such as pancreatitis. Irreversible electroporation (IRE) is a non-thermal alternative. We investigated tissue responses in a porcine pancreas model, focusing on cell death, thermal effects, and fibrosis. Methods: Seven pigs underwent pancreatic IRE via open surgery. Local tissue temperature was monitored near the electrode. Histological evaluation included H&E, TUNEL (apoptosis), Ki-67 (proliferation), vimentin (fibroblast activation), and insulin staining. Tissue remodeling was assessed at multiple time points up to 14 days. Results: IRE induced marked apoptosis within the ablated region, peaking at day 2. The maximum measured temperature was 78.4 °C. Over two weeks, fibrosis progressed with increased collagen and fibroblast activity. Regeneration was partial, with Ki-67-positive cell proliferation and gradual loss of insulin expression, while unablated tissue showed minimal damage. Conclusions: IRE enables localized pancreatic ablation while sparing surrounding tissue. However, fibrosis limits full recovery. Limitations include small sample size, short follow-up, and species differences. Further studies are needed to refine IRE parameters and assess long-term functional outcomes.