Sparse 4-D multi-spectral optical computed tomography for accurate quantification of voxel composition

用于精确量化体素成分的稀疏 4-D 多光谱光学计算机断层扫描

• 批准号: BB/P027466/1
• 负责人: Simon Robinson
• 金额: $ 19.24万
• 依托单位: Institute of Cancer Research
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FP027466%2F1
• 关键词: Sparse; multi; spectral; optical; computed

This project is about developing a new type of microscope called a multi-spectral absorption optical computed tomography scanner.Starting with the development of x-ray computed tomography - the so called CAT scanners that revolutionised medical diagnosis in the 1970's and '80s - three-dimensional imaging has become ubiquitous in modern medicine and science. Principles of computed tomography are employed in fields as diverse as remote monitoring of flames in chemical engineering, tomb-hunting in Egyptian pyramids (via muon tomography) and non-invasive mapping of radiation dose to provide quality assurance in radiotherapy treatments.Optical computed tomography - or "optical projection tomography" (OPT) as the configuration widely used in biological microscopy has come to be known - provides high quality images that are especially useful at the so-called mesoscale, with sample dimensions of the order of cm and able to resolve details as small as a 5-10 microns. Two principal forms of OPT are available, emission and absorption tomography. This project is concerned with absorption tomography, in which a beam of light shines on a sample and we form a 3-D image from the light that passes through the sample, thus measuring the amount by which the transmitted light decreases because of optical absorption by the various tissue components in the sample.Tissue absorption occurs to different degrees at different wavelengths. Optical absorption spectra are highly distinctive and may be used to separate out important components of tissue. This technique is routinely used in traditional 2-D microscopy in order to locate regions on the slide containing different types of tissue, which are "stained" with different types of chemical in order to make them more visible and more distinct from each other. Measurements are made at a (normally, relatively small) number of discrete wavelengths and analysed by computer in a process that is know as "spectral unmixing".In order for this unmixing process to work well, we need to adjust the concentrations the chemical additives (stains) such that they let through an appropriate amount of light in the range measureable by our camera. However, for the sorts of 3-D animal model that we use in cancer, this adjustment is very difficult to do, because there are significant unknowns in the processes by which these "contrast agents" are delivered to tissue. Principles of responsible animal experimentation, involving a reduction in the number of animals used and a refinement of techniques argue strongly for the development of a "smart" readout system, as suggested in this project, to gain maximum value from each animal used.A second reason for possible failures in the unmixing process is that we might not know in advance what the spectra of the compounds to be unmixed are. The spectra of an exciting new family of contrast agent, based on gold nanoparticles, can vary when they are introduced into tissue. This is unfortunate, as it hinders our ability to make accurate measurements of nanoparticle concentration via spectral unmixing. There has been an explosion of interest in gold nanoparticles in recent years, and particularly in one of the subtypes, known as gold nanorods. These can be attached to various molecules that recognise particular signatures of cancer within the body and, thus, the hope is that by measuring accurately where the nanorods congregate in tissue, we can detect and, perhaps, quantify the amount of malignant tissue.The data acquisition and processing technique used in this project is very new in OPT. It will reduce the amount of data that need to be acquired by a factor of approximately. This is extremely important because spectral OPT involves very large datasets. Our project consists of three parts: (i) simulate and optimise the acquisition process; (ii) build the new microscope; (iii) test it in two problems of great interest in cancer.

该项目旨在开发一种称为多光谱吸收光学计算机断层扫描仪的新型显微镜。从 X 射线计算机断层扫描（即所谓的 CAT 扫描仪，它在 20 世纪 70 年代和 80 年代彻底改变了医学诊断）的发展开始，三维成像在现代医学和科学中已变得无处不在。计算机断层扫描原理应用于多种领域，例如远程监控化学工程中的火焰、埃及金字塔中的古墓搜寻（通过μ介子断层扫描）以及辐射剂量的非侵入性绘图，以提供放射治疗的质量保证。光学计算机断层扫描（或“光学投影断层扫描”（OPT），广泛用于生物显微镜的配置）提供了 高质量图像在所谓的介观尺度上特别有用，样本尺寸为厘米级，能够解析小至 5-10 微米的细节。 OPT 有两种主要形式：发射断层扫描和吸收断层扫描。该项目涉及吸收断层扫描，其中一束光照射在样品上，我们根据穿过样品的光形成 3D 图像，从而测量由于样品中各种组织成分的光学吸收而导致透射光减少的量。组织吸收在不同波长下发生不同程度。光学吸收光谱非常独特，可用于分离组织的重要成分。该技术通常用于传统的二维显微镜中，以便定位载玻片上包含不同类型组织的区域，这些区域用不同类型的化学物质“染色”，以使它们更加可见并且彼此更加不同。测量是在（通常是相对较少的）离散波长上进行的，并通过计算机在称为“光谱分解”的过程中进行分析。为了使这种分解过程顺利进行，我们需要调整化学添加剂（染色剂）的浓度，以便它们在我们的相机可测量的范围内让适量的光通过。然而，对于我们在癌症中使用的各种 3D 动物模型来说，这种调整非常困难，因为这些“造影剂”传递到组织的过程存在很大的未知数。负责任的动物实验原则，包括减少使用的动物数量和改进技术，强烈支持开发“智能”读出系统，如本项目所建议的那样，从使用的每只动物中获得最大的价值。 分离过程中可能失败的第二个原因是，我们可能事先不知道要分离的化合物的光谱是什么。基于金纳米颗粒的新型造影剂系列令人兴奋，当它们被引入组织时，其光谱会发生变化。这是不幸的，因为它阻碍了我们通过光谱分解精确测量纳米颗粒浓度的能力。近年来，人们对金纳米颗粒的兴趣激增，尤其是其中一种亚型，即金纳米棒。它们可以附着在识别体内癌症特定特征的各种分子上，因此，希望通过准确测量纳米棒在组织中聚集的位置，我们可以检测并可能量化恶性组织的数量。该项目中使用的数据采集和处理技术在 OPT 中是非常新的。它将减少大约一倍需要获取的数据量。这非常重要，因为光谱 OPT 涉及非常大的数据集。我们的项目由三个部分组成：（i）模拟和优化采集过程； (ii) 建造新的显微镜； (iii) 在两个对癌症非常感兴趣的问题上测试它。

项目成果

Modelling of the penetration of optically absorbing contrast agents into tumour spheroids to investigate the feasibility of multispectral OPT

模拟光吸收造影剂渗透到肿瘤球体中以研究多光谱 OPT 的可行性

• 期刊: Journal of Biophotonics
• 影响因子: 2.8
• 作者: Azizian M

Development of multispectral optical computed microscopy using a high-resolution phantom created with synchrotron microbeam irradiation

使用同步加速器微束照射创建的高分辨率模型开发多光谱光学计算机显微镜

• 期刊: Physics in Medicine and Biology
• 影响因子: 3.5
• 作者: Doran SJ

Imaging the penetration of gold nanoparticles into tumour spheroids using high-resolution multispectral optical projection tomography

使用高分辨率多光谱光学投影断层扫描对金纳米粒子渗透到肿瘤球体中进行成像

• 期刊: ACS Nano
• 影响因子: 17.1
• 作者: Azizian M