Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing

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

Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing

Department of Biomedical EngineeringAlthough photoacoustic endoscopy (PAE) is a great technique with a huge potential in vascular imaging, it yet has some limitation for the clinical translation. Currently, one of the shortcomings of this system is electromagnetic interference (EMI) noise, which decreases signal-to-noise ratio (SNR) and slows down the technology development. The problem can not be simply overcome by increasing the optical pulse energy, unlike in ultrasound endoscopy, due to laser safety requirements. In addition, because PAE requires a wide separation between ultrasound sensor and amplifier, it is a hard task to make PAE system without EMI noise. To accelerate the progress of PAE field development, we accessed the feasibility of deep-learning-based methods for EMI noise removal. We chose four convolutional neural networks (CNN) architectures: U-Net, Segnet, FCN-16s, FCN-8s, and concluded that modified and tuned U-Net architecture suits the best for our application. We also compared deep-learning-based approach to a classical methods of noise removal to prove CNN supremacy. Applying trained and fine-tuned U-Net allowed us to see a tiny capillary mesh-like structures in a successfully denoised 3D vasculature map image, which can be used in future for the angiogenesis studies. For the future work, as we effectively removed noise from PAE images, we also expect that if we increase training dataset, our method can be applied more broadly to many areas of photoacoustic tomography to overcome EMI noise and poor SNR.ope