Non-invasive characterisation of tissue microstructure from MRI using Deep Learning: applications to brain cancer

使用深度学习对 MRI 组织微观结构进行非侵入性表征：在脑癌中的应用

• 批准号: 2882279
• 金额: --
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2882279
• 关键词: Non; invasive; characterisation; tissue; microstructure

Non-invasive and in vivo characterisation of brain tissue microstructures is of utmost importance to medicine, neuroscience, and basic biological research. If available, it would allow us to study not only healthy tissue development but also disease stage and progression, helping specialists to determine the optimal treatment. To this end, researchers have combined magnetic resonance imaging (MRI) with biophysical models to estimate tissue characteristics at the cellular level. However, these models are based on assumptions on the size and shape of cellular components to make the problem mathematically tractable, leading to unwanted errors and degeneracy. Addressing this problem, the supervisory team is exploring a potentially disruptive methodology borrowed from materials science. It consists of measuring statistical descriptors (SDs) of tissue microstructure using signals from the MRI scanner, from which histology-like representations may be reconstructed. These SDs have the advantage of describing the statistical nature of tissue components without relying on prior assumptions on cell shapes and arrangements, with huge potential to depict microarchitectures in the living body. Nevertheless, initial experiments were unstable and computationally demanding, lasting for days even in modern computers. This PhD project will focus on developing a solution to the problem by introducing machine learning (ML) approaches: first, to generate fast tissue microstructure reconstructions based on MRI-based SDs; and second, to perform quick simulations of MRI signals for any given microstructure for optimising the MRI acquisition (i.e., maximising accuracy and precision of SDs inference). Convolutional Neural Networks will be developed to solve these issues due to their flexibility and accuracy. The framework's potential will be illustrated in the study of brain cancer microstructures, with the aim of providing unique diagnostic information. Synthetic datasets representing brain cancer tissues at different stages will be utilised to train and test the algorithms. Experiments using physical phantoms will be also performed, building the grounds for potential testing on participants towards the end of the project. Measurements will be carried out in the Connectom MRI scanner, a unique facility designed to characterise tissue microstructure available at the research centre.

脑组织微观结构的非侵入性和体内表征对于医学、神经科学和基础生物学研究至关重要。如果可行，它将使我们不仅能够研究健康组织的发育，还可以研究疾病的阶段和进展，帮助专家确定最佳治疗方法。为此，研究人员将磁共振成像（MRI）与生物物理模型相结合，以在细胞水平上估计组织特征。然而，这些模型是基于对细胞成分的大小和形状的假设，使问题在数学上易于处理，导致不必要的错误和退化。为了解决这个问题，监督团队正在探索一种从材料科学中借鉴的潜在破坏性方法。它包括使用来自MRI扫描仪的信号测量组织微观结构的统计描述符（SD），从中可以重建类似组织学的表示。这些SD具有描述组织成分的统计性质的优点，而不依赖于对细胞形状和排列的先验假设，具有描绘活体中微结构的巨大潜力。尽管如此，最初的实验是不稳定的，计算要求很高，即使在现代计算机上也要持续几天。这个博士项目将专注于通过引入机器学习（ML）方法来开发问题的解决方案：首先，基于基于MRI的SD生成快速组织微结构重建;其次，针对任何给定的微结构执行MRI信号的快速模拟，以优化MRI采集（即，最大化SD推断的准确度和精确度）。卷积神经网络由于其灵活性和准确性将被开发来解决这些问题。该框架的潜力将在脑癌微观结构的研究中得到说明，目的是提供独特的诊断信息。代表不同阶段脑癌组织的合成数据集将用于训练和测试算法。还将进行使用物理幻影的实验，为项目结束时对参与者进行潜在测试奠定基础。测量将在Connectom MRI扫描仪中进行，这是一种独特的设备，旨在研究研究中心提供的组织微观结构。