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，使疑似癌症能够在不手术的情况下被诊断出来，或者及早确定治疗是否有效，这是我们可能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。核磁共振成像经常用于癌症，以了解肿瘤在化疗或放射治疗等治疗后是否生长或缩小。人们一直在开发使用核磁共振的新方法；其中一项新的发展是一种名为微结构核磁共振的技术。这项技术允许研究人员和临床医生测量体内组织结构是如何变化的。例如，测量细胞的大小，或特定组织部分中血管的方向。这些组织结构的测量是健康功能或疾病进展的重要指标。在癌症这样的疾病中，肿瘤的样本或活组织检查是由外科医生采集的。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癌症的严重程度或对其分级。能够用核磁共振进行这些测量将意味着肿瘤活检可以很容易地完成，而不需要手术。此外，组织结构的细微变化可能表明，肿瘤对特定治疗的反应早在常规MRI检测到肿瘤缩小之前很久。为此，使用显微结构MRI可以帮助识别无效的治疗方法，并允许更快地更换治疗方法，希望能为患者带来更好的结果。显微结构MRI已经在临床上进行了尝试，并取得了一些成功，但到目前为止，它的使用突显了某些组织结构测量的重复性较低或变化原因不明。我的目标是调查这种可变性。使用MRI发现组织结构的方式依赖于计算机模型来确定哪些组织结构会产生所获取的MRI信号。为了调查为什么特定的输出是不同的，需要知道实际组织结构或基本事实的数据，并可以与MRI输出进行比较。理想情况下，这种已知的组织结构可以以一种精确的方式改变，然后看看这种已知的改变如何影响MRI的输出。然而，获取这些已知的组织结构或地面真相是非常困难的。为了绕过丢失地面真相数据的问题，研究人员使用了虚拟核磁共振。虚拟核磁共振可以采用任何数字构造的形状，并模拟你将从它获得的核磁共振。这些数字构造的形状可以使其看起来像组织，并且可以很容易地进行操纵，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，一组完美的球体表示细胞，圆柱体表示血管。这并不代表真实组织的复杂性，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这些数字组织的库，这些组织基于我将从真实组织中提取的形状。为了做到这一点，我将使用我开发的3D成像技术。它使用显微镜为整个小肿瘤拍摄单细胞规模的组织3D图像(约2厘米3)。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换为数字版本。这些数字组织然后可以用来进行许多虚拟磁共振成像，以了解MRI信号和潜在组织结构之间的复杂关系。该方法结合了数字组织的灵活性和真实组织结构的复杂性。

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.