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，可以无创地提供大脑的详细图像。然而，它受到分辨率的限制（大约）。1mm）。开发能够将图像特征与微米尺度下的潜在细胞信息联系起来的模型，将大大提高诊断的特异性和灵敏度，并有助于为个别患者量身定制治疗方法。我们的目标是通过直接比较MRI和三维显微镜来开发这样一个模型。然后，我们将在中风动物模型上测试该框架。中风是世界范围内第二大死亡原因，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，这是一个最佳的临床案例来验证我们的模型，从而导致准确和精确的成像为基础的生物标志物的发展。学生将从事离体和体内（取决于申请人的经验和愿望）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，并学习实用的实验室程序，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.