AI-powered brain microstructure imaging

该奖学金发展创新的计算方法和磁共振（MR）技术，以揭示新的非侵入性大脑微观结构标记。最终目标是为改进的诊断信息提供非侵入性工具，其功能与侵入性技术一样强大。大自然创造了我们所能想象到的最非凡的机器之一：大脑，只用了神经元和神经胶质的基本细胞成分。了解这些单独的组成部分是如何设计的（细胞形态）和组装在一起（组织微观结构）是理解大脑结构和功能的关键，更重要的是它在疾病中的退化/失调。然而，目前还不可能以非侵入性的方式量化组织微观结构。事实上，像组织学这样的标准方法可以揭示组织结构的微观特征，但代价是像活组织检查这样的侵入性干预，以及对被调查组织的有限覆盖，从而削弱了诊断能力。相比之下，基于弥散性MRI （dMRI）的最新技术为水扩散过程提供了间接但非侵入性的微米级组织微观结构探测。这使得获得的dMRI信号对细胞大小/形状、密度等组织特征敏感。不幸的是，组织微观结构非常复杂，而dMRI信号非常简单，因此从信号到微观结构的映射（反问题）是不适定的。这代表了基于dMRI的技术取代组织学作为无害诊断工具的主要障碍。为了克服这些限制，目前的微观结构成像范例使用数学模型，将dMRI信号与潜在的组织特性联系起来，通过逐体素拟合模型与dMRI数据来估计和映射这些特性。然而，有三个主要的限制阻碍了它的诊断能力：对不能用数学模型描述的复杂组织特征的敏感性差；缺乏对不同细胞类型的特异性，并且在反问题的解决方案中存在模糊性。在这项研究中，我提出了一个由三部分组成的范式转变，以解决这些关键限制：(I)采用组织结构的详细模拟来编码正向问题（从组织微观结构到MR信号），（ii）现代人工智能来解决逆向问题，（iii）估计不确定性以量化结果的模糊性和重要性。我将在大脑中通过模拟正常组织环境和代表常见神经系统疾病的病理，如多发性硬化症（MS）和阿尔茨海默病（AD）来演示这一点。这将为新的和重要的微观结构特征提供更高的灵敏度，如细胞体细胞大小/密度和神经突大小/复杂性，从而导致基于水扩散的新一代定量成像技术。为了获得对大脑中不同细胞类型的前所未有的特异性，并开发一套新的神经系统疾病成像标记，我将使用dMR光谱（dMRS）将新范式与代谢物扩散测量相结合。事实上，代谢物是比水更具有细胞特异性的分子：一些主要存在于神经元中，另一些主要存在于神经胶质中。我将在多发性硬化症和阿尔茨海默病的受控动物模型中对新技术进行原型和验证，并最终在人类患者中提供概念验证应用。这些创新技术在未来几十年为实现“虚拟组织学”在广泛的医学应用提供了巨大的希望。虽然该奖学金的重点是神经系统疾病，但它也旨在启动后续项目，探索其他应用，如身体癌症。替代对比方法将把方法扩展到扩散以外的其他MR模式，以获得关于健康和病变组织的补充和额外信息。

This fellowship develops innovative computational methods and magnetic resonance (MR) techniques to reveal new non-invasive markers of brain microstructure. The ultimate goal is to provide non-invasive tools for improved diagnostic information as powerful as invasive techniques.Nature has built one of the most extraordinary machines we could ever conceive: the brain, using just basic cellular components of neurons and glia. Understanding how these individual components are designed (cell morphology) and assembled together (tissue microstructure) is the key to understanding both brain's structure and function, and more importantly its degeneration/dysregulation in diseases. However, it is currently impossible to quantify tissue microstructure in a non-invasive way. In fact, standard methods like histology can reveal microscopic characteristics of tissue architecture but at the cost of invasive interventions like biopsies and limited coverage of the investigated tissue, undermining diagnostic power. In contrast, sensitizing the MR imaging (MRI) contrast to water diffusion process, state-of-the-art technologies based on diffusion MRI (dMRI) provides an indirect but non-invasive probe of the tissue microstructure at the micrometer scale. This makes the acquired dMRI signal sensitive to tissue features like cellular size/shape, density, etc. Unfortunately, the tissue microstructure is highly complex while the dMRI signal is quite simple, so the mapping from signal to microstructure (inverse problem) is ill-posed. This represents a major obstacle for dMRI based techniques to replace histology as harmless diagnostic tools.To overcome these limitations, the current paradigm of microstructure imaging uses mathematical models, which relate the dMRI signal to underlying tissue properties, to estimate and map those properties by fitting the models voxel-by-voxel to dMRI data. However, there are three main limitations hampering its diagnostic power: poor sensitivity to complex tissue features that cannot be described by mathematical models; a lack of specificity to different cell types, and ambiguity in the inverse problem's solution. In this fellowship I propose a three-component shift of paradigm to address these key limitations: (i) employing detailed simulation of the tissue architecture to encode the forward problem (from tissue microstructure to MR signal), (ii) modern AI to solve the inverse problem and (iii) estimate of uncertainty to quantify ambiguity and significance of the results. I will demonstrate this in the brain by simulating normal tissue environments and those representing pathologies of common neurological diseases, like Multiple Sclerosis (MS) and Alzheimer's disease (AD). This will provide higher sensitivity to novel and important microstructural features like cell soma size/density and neurites size/complexity, leading to a new generation of quantitative imaging techniques based on water diffusion. To gain unprecedented specificity to different cell types in the brain and develop a new set of imaging markers for neurological conditions, I will combine the new paradigm with metabolites' diffusion measurements using dMR spectroscopy (dMRS). Indeed, metabolites are more cell-specific molecules than water: some are found mostly in neurons, others mostly in glia. I will prototype and validate the new technologies in controlled animal models of MS and AD and eventually provide proof-of-concept application in human patients. These innovative techniques offer great promise in the decades to come for the realisation of 'virtual histology' across a wide range of medical applications.Although the fellowship focuses on neurological diseases, it also aims to initiate follow-on projects to explore other applications, like body cancer. Alternative contrast methods will extend the methods to other MR modalities beyond diffusion for complementary and additional information on healthy and diseased tissues.