Enhancing photoacoustic imaging for lung diagnostics and BCI communication: simulation of cavity structures artifact generation and evaluation of noise reduction techniques

Enhancing photoacoustic imaging for lung diagnostics and BCI communication: simulation of cavity structures artifact generation and evaluation of noise reduction techniques

Blog Article

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 Bike Parts - Pedals - Platform 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 Pygeum 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.