Advanced X-Ray Micro-CT Methods for The Delineation of Tumour Heterogeneity in Lung Cancer

用于描绘肺癌肿瘤异质性的先进 X 射线显微 CT 方法

• 批准号: 2578663
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2578663
• 关键词: Advanced; Ray; Micro; CT; Methods

1) Brief description of the context of the research including potential impactAnalysis of tumour texture heterogeneity using machine learning techniques with CT-images shows potential for the prediction of cancer malignancy and aggressiveness [1], which may inform patient treatment plans. Phase-contrast x-ray techniques, which derive image contrast from phase-shifts in propagating x-rays [2], provide improved sensitivity to texture and microstructure in samples that would appear mostly homogenous to conventional, attenuation-based x-ray imaging. This project proposes to investigate x-ray phase-contrast micro-CT as a method to image ex-vivo lung samples and produce three dimensional representations. The new approach will be compared to the current histopathological standard, which will be the ground truth. Additionally, machine learning and other advanced computational techniques will be applied for the registration, segmentation, and classification of the images. This work will contribute to the efforts in scaling up these techniques towards new in-vivo clinical applications.2) Aims and ObjectivesThe project will entail a bottom-up approach, starting with the modelling, optimisation and fine tuning of the imaging setups. Image retrieval algorithm development will follow, along with an imaging protocol for a pilot study with clinical tissue samples. From both modelling and the pilot data on ex-vivo tissue samples, it will be possible to extrapolate a comprehensive system design and specification for the prospective in-vivo implementation of the technique.3) Novelty of Research MethodologyThe edge-illumination technique has been applied to the phase-contrast x-ray imaging of ex-vivo tissue samples [3][4], with its capabilities extended to tomographic imaging [5]. Building on these milestones, this project will exploit the high-resolution and texture of phase-contrast images to categorise tumour heterogeneity in lung cancer.4) Alignment to EPSRC's strategies and research areasThis is a strongly interdisciplinary project, aligning with EPSRC themes across the domains of engineering, physical sciences, and healthcare technologies. Key areas of research are medical imaging, sensors and instrumentation, and artificial intelligence technologies.5) Any companies or collaborators involvedThe infrastructure available to this project includes a first commercial prototype X-ray phase-contrast scanner through the UCL-Nikon Prosperity Partnership, and two unique custom-built X-ray

1)研究背景的简要描述，包括潜在的影响使用机器学习技术与CT图像对肿瘤纹理异质性的分析显示了预测癌症恶性程度和侵袭性的潜力[1]，这可能会为患者的治疗计划提供信息。相衬X射线技术从传播X射线的相移中获得图像对比度[2]，提高了对样品中纹理和微观结构的灵敏度，这些纹理和微观结构与传统的基于衰减的X射线成像相比看起来基本上是均匀的。该项目提出研究X射线相衬显微CT作为一种方法来成像离体肺样本，并产生三维表示。新方法将与当前的组织病理学标准进行比较，这将是地面真相。此外，机器学习和其他先进的计算技术将用于图像的配准、分割和分类。这项工作将有助于努力扩大这些技术对新的体内临床应用。2）目的和目标该项目将需要一个自下而上的方法，从建模，优化和微调的成像设置。图像检索算法开发将遵循，沿着临床组织样本初步研究的成像方案。根据离体组织样本的建模和试验数据，可以推断出该技术的预期体内实施的综合系统设计和规范。3）研究方法的新奇边缘照明技术已应用于离体组织样本的相衬X射线成像[3][4]，其功能扩展到断层成像[5]。在这些里程碑的基础上，该项目将利用相位对比图像的高分辨率和纹理对肺癌中的肿瘤异质性进行分类。4）与EPSRC的策略和研究领域保持一致这是一个跨学科的项目，与EPSRC的工程、物理科学和医疗技术领域的主题保持一致。主要研究领域是医学成像、传感器和仪器以及人工智能技术。5）任何参与的公司或合作者该项目可用的基础设施包括通过UCL-尼康繁荣合作伙伴关系提供的第一台商业原型X射线相衬扫描仪，以及两台独特的定制X射线相衬扫描仪。