Label-Free Intraoperative Histological Imaging

无标记术中组织学成像

• 批准号: 2889278
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889278
• 关键词: Label; Free; Intraoperative; Histological; Imaging

This project is based on the scientific development of a new histological imaging technique, which will be used to minimise the sampling errors and provide a faster and more accurate intraoperative assessment of the tumour margin. Computational microscopy and image reconstruction strategies will be used to enable high-information content, label-free histological imaging using the mesolens, a giant objective lens used and developed at the University of Strathclyde. By combing an array of LED's for multimodal illumination with physics and deep learning strategies, specifically, convolution neural networks (CNN), will allow strategies for image reconstruction and analysis.Our goal for the project is to enable high-quality diagnostic imaging of multi-mm scale tissue biopsies in under 5 minutes.The project will be supervised by Professor Gail McConnell (University of Strathclyde), Dr Michael Shaw (NPL) and Dr Catriona Douglas (NHS).Additionally, training is provided in many key areas for the project such as, tissue culture and cell biology, optical engineering, biomedical imaging, and computational data analysis.

该项目基于一种新的组织学成像技术的科学发展，该技术将用于最大限度地减少采样误差，并提供更快、更准确的术中肿瘤边缘评估。计算显微镜和图像重建策略将用于使用mesolens（一种由斯特拉斯克莱德大学使用和开发的巨大物镜）实现高信息量、无标签的组织学成像。通过将一系列用于多模态照明的LED与物理和深度学习策略（特别是卷积神经网络（CNN））相结合，将允许图像重建和分析策略。我们的目标是在5分钟内实现多毫米组织活检的高质量诊断成像。该项目将由Gail McConnell教授（斯特拉斯克莱德大学）、Michael Shaw博士（英国国家物理实验室）和Catriona Douglas博士（英国国家医疗服务体系）监督。此外，该项目还提供了许多关键领域的培训，如组织培养和细胞生物学、光学工程、生物医学成像和计算数据分析。