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Yang, Joon Mo
광음향 의료기기 연구실
Research Interests
  • Biomedical imaging, Biomedical optics, Photoacoustic imaging, Ultrasound imaging, Novel endoscopic techniques

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

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dc.contributor.author Gulenko, Oleksandra ko
dc.contributor.author Yang, Hyunmo ko
dc.contributor.author Kim, KiSik ko
dc.contributor.author Youm, Jin Young ko
dc.contributor.author Kim , Minjae ko
dc.contributor.author Kim, Yunho ko
dc.contributor.author Jung, Woonggyu ko
dc.contributor.author Yang, Joon-Mo ko
dc.date.available 2022-05-27T02:14:56Z -
dc.date.created 2022-05-24 ko
dc.date.issued 2022-05 ko
dc.identifier.citation SENSORS, v.22, no.10, pp.3961 ko
dc.identifier.issn 1424-8220 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/58555 -
dc.description.abstract 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. ko
dc.language 영어 ko
dc.publisher MDPI ko
dc.title Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-85130340174 ko
dc.identifier.wosid 000801592500001 ko
dc.type.rims ART ko
dc.identifier.doi 10.3390/s22103961 ko
dc.identifier.url https://www.mdpi.com/1424-8220/22/10/3961 ko
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