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2023 World Conference on Lung Cancer (Posters)
P1.26. Random Matrix Based Multiple Scattering Com ...
P1.26. Random Matrix Based Multiple Scattering Component Extraction Can Significantly Shorten the Time of Lung Segmentectomy - PDF(Abstract)
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This study focuses on the use of random matrix based multiple scattering component extraction to shorten the time of lung segmentectomy. The multiple scattering of coherent waves in lung tissue affects the transmission process, and the Brownian motion generated by scatters like red blood cells perturbs the photon propagation trajectory and forms dynamic speckle. In segmental resection, determining the lung segment is challenging. The researchers used random matrix theory to study the characteristics of backscattered light at different time points after pulmonary artery occlusion. They collected data using a laparoscope with dual sensors and analyzed the data using a random matrix constructed based on the infrared channel strength values. The statistical characteristics of single and multiple scattering components were estimated. The results showed that the white light pattern and multiple scattering components were the lowest after arterial occlusion for 0 minutes and increased after 5 and 10 minutes of occlusion. The study concludes that random matrix based multiple scattering component extraction can significantly shorten the time of lung segmentectomy. Keywords for this study include random matrix, scattering component, and lung segmentectomy. Overall, this research explores a new approach to improve the efficiency of lung segmentectomy, which could potentially benefit patients with early-stage non-small cell lung cancer.
Asset Subtitle
Hua Liu
Meta Tag
Speaker
Hua Liu
Topic
Early-Stage NSCLC: More Minimally Invasive Approaches
Keywords
random matrix
scattering component
lung segmentectomy
coherent waves
lung tissue
transmission process
Brownian motion
red blood cells
photon propagation trajectory
dynamic speckle
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