AI-powered brain microstructure imaging

人工智能驱动的大脑微结构成像

• 批准号: MR/T020296/2
• 负责人: Marco Palombo
• 金额: $ 113.45万
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT020296%2F2
• 关键词: AI; powered; brain; microstructure; imaging

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.

该研究金开发创新的计算方法和磁共振（MR）技术，以揭示大脑微观结构的新非侵入性标记。最终的目标是提供非侵入性的工具，以改善诊断信息，与侵入性技术一样强大。大自然已经建造了我们所能想象的最非凡的机器之一：大脑，只使用神经元和神经胶质的基本细胞成分。了解这些单独的组件是如何设计的（细胞形态）和组装在一起（组织微观结构）是了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，目前不可能以非侵入性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，但代价是侵入性干预，如活组织检查和所研究组织的有限覆盖范围，削弱了诊断能力。相比之下，基于扩散MRI（dMRI）的最新技术使MR成像（MRI）对水扩散过程的对比敏感，提供了在微米尺度上对组织微观结构的间接但非侵入性的探测。这使得采集的dMRI信号对组织特征（如细胞大小/形状、密度等）敏感。不幸的是，组织微观结构非常复杂，而dMRI信号相当简单，因此从信号到微观结构的映射（逆问题）是不适定的。为了克服这些局限性，目前的微结构成像范例使用数学模型，它将dMRI信号与潜在的组织属性相关联，通过将模型逐体素拟合到dMRI数据来估计和映射这些属性。然而，有三个主要的限制阻碍了它的诊断能力：对复杂组织特征的敏感性差，无法用数学模型描述;缺乏对不同细胞类型的特异性，以及逆问题解决方案的模糊性。在这项研究中，我提出了一个三个组成部分的范式转变来解决这些关键限制：（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AI来解决逆向问题，（iii）估计不确定性来量化结果的模糊性和重要性。我将通过模拟正常组织环境和常见神经系统疾病（如多发性硬化症（MS）和阿尔茨海默病（AD））的病理学来证明这一点。这将提供对新的和重要的微观结构特征如细胞索马尺寸/密度和神经突尺寸/复杂性的更高灵敏度，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的成像标记物，我将联合收割机与使用dMR波谱（dMRS）的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MS和AD的受控动物模型中原型化和验证新技术，并最终在人类患者中提供概念验证应用。这些创新技术为未来几十年在广泛的医疗应用中实现“虚拟组织学”提供了巨大的希望。虽然该奖学金专注于神经系统疾病，但它也旨在启动后续项目，以探索其他应用，如身体癌症。替代对比方法将扩展该方法到扩散以外的其他MR模态，以获得关于健康和患病组织的补充和附加信息。

项目成果

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge.

• DOI: 10.1016/j.neuroimage.2021.118367
• 发表时间: 2021-10-15
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: De Luca A;Ianus A;Leemans A;Palombo M;Shemesh N;Zhang H;Alexander DC;Nilsson M;Froeling M;Biessels GJ;Zucchelli M;Frigo M;Albay E;Sedlar S;Alimi A;Deslauriers-Gauthier S;Deriche R;Fick R;Afzali M;Pieciak T;Bogusz F;Aja-Fernández S;Özarslan E;Jones DK;Chen H;Jin M;Zhang Z;Wang F;Nath V;Parvathaneni P;Morez J;Sijbers J;Jeurissen B;Fadnavis S;Endres S;Rokem A;Garyfallidis E;Sanchez I;Prchkovska V;Rodrigues P;Landman BA;Schilling KG
• 通讯作者: Schilling KG

SPHERIOUSLY? The challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI.

• DOI: 10.1016/j.neuroimage.2021.118183
• 发表时间: 2021-08-15
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Afzali M;Nilsson M;Palombo M;Jones DK
• 通讯作者: Jones DK

Comprehensive Brain Tumour Characterisation with VERDICT-MRI: Evaluation of Cellular and Vascular Measures Validated by Histology.

• DOI: 10.3390/cancers15092490
• 发表时间: 2023-04-27
• 期刊: Cancers
• 影响因子: 5.2

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge

关于跨采集参数、序列和组织类型的扩散 MRI 信号表示的普遍性：MMENTO 挑战的编年史

• DOI: 10.1101/2021.03.02.433228
• 发表时间: 2021
• 作者: De Luca A

Neural Networks for parameter estimation in microstructural MRI: a study with a high-dimensional diffusion-relaxation model of white matter microstructure

• DOI: 10.1101/2021.03.12.435163
• 发表时间: 2021-03
• 期刊: bioRxiv
• 作者: João P. de Almeida Martins;M. Nilsson;Björn Lampinen;M. Palombo;P. T. While;C. Westin;F. Szczepankiewicz