Creating high fidelity digital tissue substrates for the development of non-invasive microstructural MRI

癌症是21世纪发达国家面临的主要卫生保健挑战之一。开发新的成像技术，使可疑的癌症可以在不手术的情况下被诊断出来，或者在早期确定治疗是否有效，是我们应对这一挑战的重要方法。磁共振成像（MRI）是一种已经在医院广泛使用的技术。MRI通常用于癌症，以观察肿瘤是否会因化疗或放疗等治疗而生长或缩小。使用MRI的新方法一直在开发;一个新的发展是一种称为显微结构MRI的技术。这项技术使研究人员和临床医生能够测量体内组织结构的变化。例如，测量细胞的大小，或组织特定部分中血管的方向。这些组织结构的测量是健康功能或疾病进展的重要指示。在癌症等疾病中，肿瘤的样本或活检由外科医生进行。活检是由组织学家在显微镜下观察的，组织学家可以使用细胞和血管的形状和大小来诊断或分级癌症的严重程度。能够用MRI进行这些测量意味着肿瘤活检可以很容易地完成而无需手术。此外，组织结构的细微变化可以表明肿瘤对特定治疗的反应早于常规MRI检测到肿瘤缩小。使用显微结构MRI可以帮助识别不起作用的治疗方法，并允许他们更快地改变，希望为患者带来更好的结果。显微结构MRI已经在临床上尝试并取得了一些成功，但到目前为止，它的使用突出了某些组织结构测量似乎具有低重复性或以无法解释的方式变化。我的目的是研究这种变化。使用MRI发现组织结构的方式依赖于计算机模型来确定什么组织结构会产生所获取的MRI信号。为了研究特定输出变化的原因，需要已知实际组织结构或真实情况的数据，并将其与MRI输出进行比较。理想情况下，这种已知的组织结构将是可以以精确的方式改变的组织结构，然后查看已知的变化如何影响MRI输出。然而，获取这些已知的组织结构或真实数据是非常困难的，为了解决这个问题，研究人员使用了虚拟MRI。虚拟MRI可以采用任何数字构建的形状，并模拟您将从中获得的MRI。这些数字构建的形状可以看起来像组织，并且可以很容易地进行操作，以研究组织结构中的某些类型的变化。到目前为止，研究人员使用非常简单的数字形状来表示组织，例如，一组完美的球体来表示细胞，圆柱体来表示血管。这并不代表真实的组织的复杂性，许多研究人员认为这可能是迄今为止临床试验困难的原因。我的奖学金旨在创建一个基于我将从真实的组织中提取的形状的这些数字组织的库。为此，我将使用我开发的3D成像技术。它使用显微镜拍摄整个小肿瘤（~ 2cm 3）的单细胞尺度的组织3D图像。我的目标是从这些图像中提取细胞和血管的形状，然后通过计算机模型将其转化为数字版本。然后，这些数字组织可以用于进行许多虚拟MRI，以了解MRI信号与底层组织结构之间的复杂关系。这种方法结合了数字组织的灵活性和真实的组织结构的复杂性。

Cancer is one of the developed world's leading health care challenges for the 21st Century. Developing new imaging techniques that allow suspected cancers to be diagnosed without surgery, or determine early on whether a treatment is working, are important ways we may tackle this challenge.Magnetic resonance imaging (MRI) is a technique that is already widely used in hospitals. MRI is often used in cancer to see if a tumour is growing or shrinking in response to treatments like chemotherapy or radiotherapy. New ways to use MRI are always being developed; one new development is a technique called microstructural MRI. This technique allows researchers and clinicians to measure how the structure of tissues in the body are changing. For example, measuring the size of cells, or the orientation of blood vessels in a particular section of tissue. These measures of tissue structure are an important indication of healthy function or disease progression. In diseases like cancer, samples or biopsies of a tumour are taken by a surgeon. The biopsy is looked at under a microscope by a histologist who can use the shapes and sizes of the cells and blood vessels to diagnose or grade a cancer's severity. Being able to make these measurements with MRI would mean that tumour biopsies could be done easily without surgery. In addition, subtle changes in a tissues structure can indicate that a tumour is responding to a particular treatment long before a conventional MRI would detect tumour shrinkage. Using microstructural MRI for this purpose could help to identify treatments which aren't working, and allow them to be changed more quickly, hopefully leading to a better outcome for that patient.Microstructural MRI has already been tried in the clinic with some success, however its use so far has highlighted that certain tissue structure measurements seem to have low reproducibility or vary in unexplained ways. I am aiming to investigate this variability. The way in which a tissue's structure can be found using MRI relies on a computer model to determine what tissue structures would have produced the MRI signal that is acquired. In order to investigate why particular outputs are varying, data are needed where the actual tissue structure or ground truth is known and can be compared to the MRI outputs. Ideally, this known tissue structure would be one that could be changed in a precise way to then see how the known change has affected the MRI output. However, acquiring these known tissue structures or ground truths is very difficult.To get around this problem of missing ground truth data researchers have used virtual MRIs. A virtual MRI can take any digitally constructed shape and simulate the MRI you would acquire from it. These digitally constructed shapes can be made to look like tissues and can be easily manipulated by to investigate certain types of changes in tissue structure. Up till now researchers have used very simple digital shapes to represent tissues, for example a collection of perfect spheres to represent cells and cylinders to represent blood vessels. This does not represent the complexity of real tissue and many researchers believe this may be the reason for the difficulties in the clinical trial thus far.My fellowship aims to create a library of these digital tissues which are based on the shapes I will extract from real tissue. To do this I will use a 3D imaging technique that I have developed. It uses a microsope to take 3D images of tissues at the scale of single cells for whole small tumours (~2cm3). I aim to extract the shapes of the cells and blood vessels from these images which I will then turn these into digital versions through a computer model. These digital tissues can then be used to conduct many virtual MRIs to understand the complex relationship between the MRI signal and the underlying tissue structure. This approach combines the flexibility of digital tissues with the complexity of real tissue structure.