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ORIGINAL RESEARCH article
Front. Bioeng. Biotechnol.
Sec. Biosensors and Biomolecular Electronics
Volume 12 - 2024 |
doi: 10.3389/fbioe.2024.1452865
This article is part of the Research Topic Bioimaging Applications in Biosensors and Biomolecular Electronics View all 5 articles
Enhancing Photoacoustic Imaging for Lung Diagnostics and BCI Communication: Simulation of Cavity Structures Artifact Generation and Evaluation of Noise Reduction Techniques
Provisionally accepted- 1 CenBRAIN Lab, School of Engineering, Westlake University, Hangzhou, China
- 2 Westlake Institute for Advanced Study (WIAS), Hangzhou, Zhejiang Province, China
- 3 Research Center for Humanoid Sensing, Zhejiang Lab, Hangzhou, China
- 4 Cross-strait Tsinghua Research Institute, Xiamen, Fujian Province, China
Pandemics like COVID-19 have highlighted the potential of Photoacoustic imaging (PAI) for Brain-Computer Interface (BCI) communication and lung diagnostics. However, PAI struggles with the clear imaging of blood vessels in areas like the lungs and brain due to their cavity structures. This paper presents a simulation model to analyze the generation and propagation mechanism within phantom tissues of PAI artifacts, focusing on the evaluation of both Anisotropic diffusion filtering (ADF) and Non-local mean (NLM) filtering, which significantly reduce noise and eliminate artifacts and signify a pivotal point for selecting artifact-removal algorithms under varying conditions of light distribution.Experimental validation demonstrated the efficacy of our technique, elucidating the effect of light source uniformity on artifact-removal performance. The NLM filtering simulation and ADF experimental validation increased the peak signal-to-noise ratio by 11.33% and 18.1%, respectively. The proposed technique adds a promising dimension for BCI and is an accurate imaging solution for diagnosing lung diseases.
Keywords: photoacoustic imaging, Lung imaging, BCI, Monte Carlo simulation, De-artifact, adf, NLM, Phantom verification
Received: 21 Jun 2024; Accepted: 26 Aug 2024.
Copyright: © 2024 Chai, Yang, Gao, Shi, Wang, Song, Chen and Sawan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yun-Hsuan Chen, CenBRAIN Lab, School of Engineering, Westlake University, Hangzhou, China
Mohamad Sawan, CenBRAIN Lab, School of Engineering, Westlake University, Hangzhou, China
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