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

磁共振成像(MRI)是一种重要的诊断和研究工具，它可以非侵入性地提供大脑的详细图像。但是，它受到分辨率的限制(约1 mm)。开发能够将图像特征与微米级别的潜在细胞信息联系起来的模型将显著提高诊断的特异性和敏感性，并有助于针对个别患者进行量身定制的治疗。我们的目标是通过直接将磁共振成像与三维显微镜进行比较来开发这样的模型。然后，我们将在中风动物模型上测试该框架。中风是世界范围内的第二大死因，它会导致复杂的脑损伤模式，并伴随着不同程度的细胞再生。因此，这是一个最好的临床案例来验证我们的模型，导致基于准确和精确成像的生物标记物的开发。学生将从事体外和体内(取决于申请者的经验和意愿)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，并学习使大脑透明的实用实验室程序。这将允许学生使用3D显微镜图像对MR图像的特征进行建模。

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.