A machine learning approach for the quantification of tumour microstructure from diffusion-weighted MRI

研究背景的简要描述，包括潜在影响扩散加权MRI使用水的扩散运动来评估组织微观结构。通过使MR扫描仪对水的有限扩散敏感，可以诱导信号对比，从而描绘组织微观结构和量化细胞大小/密度等特性。这些信息可用于创建肿瘤分级、预后和治疗反应的生物标志物，这些生物标志物在临床中具有潜在的应用价值，因为它们都是迫切需要的。从组织微结构图像中模拟MR信号有助于量化信号方法的发展。这种基于模拟的方法消除了耗时的MRI数据实验采集，并使扫描参数的优化操作成为可能。模拟数据还减少了不一致的可能性，从而使生成的信号具有可重复性和无伪影性。目的和目的传统的组织微观结构模型具有简单的几何形状，已被用于分析扩散加权信号。我的目标是消除这样的模型，它们可能是简化的，而是使用机器学习的另一种方法来提取组织参数。蒙特卡罗模拟将用于从3D高分辨率数据集生成扩散磁共振信号，通过“随机漫步”测量分子从原始起点在确定时间内的位移。这将用于各种MR脉冲序列，以提供来自结直肠组织的单一高分辨率组织微观结构的多个MR数据集。该合成MRI数据将用于训练，测试和验证监督机器学习网络，以根据MR信号的输入向量输出肿瘤微观结构属性的估计。训练将允许对以前未见过的数据进行参数估计。研究方法的新颖性蒙特卡罗模拟已经使用多年来合成MR信号，在复杂的几何形状中包含血流和模拟是最近的发展，目前没有可用的，完整和开放的库，涵盖了MRI采集中使用的大范围序列，并且特定于结直肠癌，这是英国第四大常见癌症，使其成为一种非常可行的疾病。虽然在过去十年中，用于图像分类和分割的机器学习方法越来越广泛，但它们在医学成像中的应用仍处于起步阶段，而在MR信号量化中的应用则是一个更小的领域。此外，在这方面所做的研究几乎完全集中在脑和中枢神经组织的磁共振应用上，而癌症的微观结构尚未研究。与EPSRC的战略和研究领域保持一致该项目与EPSRC对多学科研究的关注保持一致。实践方面涉及计算机科学和通过机器学习的人工智能。这个项目的应用在医疗保健技术中占有一席之地，并有望成为评估癌症等疾病的未来。

Brief Description of the context of the research including potential impactDiffusion-weighted MRI uses the diffusive motion of water to assess tissue microstructure. By sensitising the MR scanner to the restricted diffusion of water, signal contrast can be induced allowing the delineation of tissue microstructure and quantification of properties such as cell size/ density. This information can be used to create biomarkers of tumour grade, prognosis and treatment response which have potential implementation in clinic where they are all critically required.Simulating MR signal from images of tissue microstructures facilitates the development of methods to quantify the signal. This simulation based approach eliminates time consuming experimental acquisition of MRI data and enables manipulation of scan parameters for optimisation. Simulated data also reduces the likelihood of inconsistencies allowing generated signal to be reproducible and artefact-free. Aims and ObjectivesTraditionally models of tissue microstructure with simplistic geometries have been used to analyse diffusion weighted signals. My aim is to eliminate such models, which can be reductionist, and instead use an alternative approach of machine learning to extract tissue parameters. Monte Carlo simulations will be used to generate Diffusion MR signals from 3D high resolution datasets by measuring molecule displacement from their original starting point over a determined amount of time through 'random walks'. This will be done for a variety of MR pulse sequences to provide multiple MR datasets from a single high resolution tissue microstructure of colorectal tissue.This synthetic MRI data will be used to train, test and validate a supervised machine learning network to output estimations of tumour microstructure properties dependent on the input vector of MR signal. The training will allow for parameter estimation on previously unseen data. Novelty of the Research MethodologyWhere Monte Carlo simulations have been used for years to synthesise MR signal, the inclusion of blood flow and simulation in complex geometries is a more recent development for which there is currently no available, complete and open library which covers the large range of sequences used in MRI acquisition and is specific to colorectal cancer, the 4th most common cancer in the UK, making it an extremely viable disease to look at. Whilst machine learning approaches for image classification and segmentation have increasingly widespread in the past decade, their application to medical imaging is still in its infancy and their use in the quantification of MR signals is an even smaller field. In addition, what research has been done into this has almost exclusively focused on application to MR of brain and central nervous tissue whilst cancer microstructure has not yet been researched.Alignment to EPSRC's strategies and research areasThis project aligns with the EPSRC focus on multi-disciplinary research. The practical aspects involve computer science and artificial intelligence via machine learning. The applications of this project have place in healthcare technologies and look to be the future of assessing diseases such as cancer.