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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2411.16039 |
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| _version_ | 1866914096987766784 |
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| author | Shi, Yan Zhao, Denghui Yu, Jingyi Ni, Wei Li, Pengcheng Gu, Yun Miao, Peng Tong, Shanbao |
| author_facet | Shi, Yan Zhao, Denghui Yu, Jingyi Ni, Wei Li, Pengcheng Gu, Yun Miao, Peng Tong, Shanbao |
| contents | Intraoperative visualization of hemodynamics is crucial for accurate diagnosis and informed surgical decision-making. In neurosurgery, indocyanine green fluorescence imaging (ICG-FI) is the gold standard for assessing blood flow and identifying vascular structures. However, it is limited by time-consuming data acquisition, mandatory waiting periods, potential allergic reactions, and operational complexities. Label-free alternatives, such as laser speckle contrast imaging (LSCI) and white light imaging (WLI), offer real-time vascular assessment but cannot resolve arterial-venous differentiation or blood flow direction determination. To address these challenges, we present a label-free cross-modal generation framework to synthesize mean transition time (MTT) maps from LSCI and WLI. MTT maps encode local hemodynamics, enabling artery-vein differentiation and flow direction inference. Experimental validation in rat brains demonstrates that the proposed method presents clear vasculature delineation, accurate artery-vein differentiation, and reliable blood flow direction decoding, while reducing total imaging time by 95.8% compared to conventional ICG protocols. This approach offers a fast, efficient, and contrast-free solution for continuous intraoperative surgical guidance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16039 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Label-Free Intraoperative Imaging of Hemodynamics using Deep Learning Shi, Yan Zhao, Denghui Yu, Jingyi Ni, Wei Li, Pengcheng Gu, Yun Miao, Peng Tong, Shanbao Medical Physics Intraoperative visualization of hemodynamics is crucial for accurate diagnosis and informed surgical decision-making. In neurosurgery, indocyanine green fluorescence imaging (ICG-FI) is the gold standard for assessing blood flow and identifying vascular structures. However, it is limited by time-consuming data acquisition, mandatory waiting periods, potential allergic reactions, and operational complexities. Label-free alternatives, such as laser speckle contrast imaging (LSCI) and white light imaging (WLI), offer real-time vascular assessment but cannot resolve arterial-venous differentiation or blood flow direction determination. To address these challenges, we present a label-free cross-modal generation framework to synthesize mean transition time (MTT) maps from LSCI and WLI. MTT maps encode local hemodynamics, enabling artery-vein differentiation and flow direction inference. Experimental validation in rat brains demonstrates that the proposed method presents clear vasculature delineation, accurate artery-vein differentiation, and reliable blood flow direction decoding, while reducing total imaging time by 95.8% compared to conventional ICG protocols. This approach offers a fast, efficient, and contrast-free solution for continuous intraoperative surgical guidance. |
| title | Label-Free Intraoperative Imaging of Hemodynamics using Deep Learning |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2411.16039 |