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A cross-resolution preclinical imaging framework based on MRI and light sheet microscopy to assess post-stroke axonal regrowth

基于 MRI 和光片显微镜的跨分辨率临床前成像框架，用于评估中风后轴突再生

基本信息

批准号：
2448439
负责人：
金额：
--
依托单位：
University of Nottingham
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2448439
关键词：
cross resolution preclinical imaging framework

项目摘要

Magnetic Resonance Imaging (MRI) is an important diagnostic and research tool that provides detailed images of the brain non-invasively. However, it is limited by resolution (approx. 1mm). The development of models that can link features of the image to underlying cellular information at the micrometre scale would significantly improve specificity and sensitivity of diagnosis and help to tailor treatments to individual patients. We aim to develop such a model by directly comparing MRI to three-dimensional microscopy. Then, we will test the framework on a stroke animal model. Stroke is the second cause of death worldwide and produces a complex pattern of brain injury followed by various degrees of cellular regrowth. It is therefore an optimal clinical case to validate our model, leading to the development of accurate and precise imaging-based biomarkers.The student will engage in specialist training with ex vivo and in vivo (subject to the experience and wishes of the applicant) animal handling, preclinical imaging, and data analysis techniques. The successful candidate will also acquire new computational and modelling skills as well as learn practical laboratory procedures to make a brain transparent. This will allow the student to model features of the MR images using 3D microscopy images.

磁共振成像（MRI）是一种重要的诊断和研究工具，可以提供无创的大脑详细图像。然而，它受到分辨率的限制。1毫米)。能够在微米尺度上将图像特征与潜在细胞信息联系起来的模型的发展将显著提高诊断的特异性和敏感性，并有助于为个体患者量身定制治疗。我们的目标是通过直接比较MRI和三维显微镜来建立这样一个模型。然后，我们将在中风动物模型上测试该框架。中风是全球第二大死亡原因，并产生复杂的脑损伤模式，随后是不同程度的细胞再生。因此，这是验证我们模型的最佳临床病例，从而导致准确和精确的基于成像的生物标志物的发展。学生将参与离体和体内（根据申请人的经验和意愿）动物处理、临床前成像和数据分析技术的专业培训。成功的候选人还将获得新的计算和建模技能，以及学习实际的实验室程序，使大脑透明。这将允许学生使用3D显微镜图像建模MR图像的特征。