CardiacA.I.: Machine learning for the analysis of multimodal cardiac MR images used in the diagnosis of coronary heart disease

久坐不动的生活方式、不良饮食、吸烟、遗传和其他健康因素是冠心病（CHD）的主要原因。尽管与过去几十年相比，最近的医学进步降低了死亡人数，但CHD仍然是英国死亡率第一的疾病（每年73，000例死亡），并带来巨大的经济负担：估计每年英国经济的成本为67亿英镑。该项目的首要目标是利用心脏磁共振图像中的多模态信息来改善其分析，促进冠心病的诊断和改善治疗。磁共振成像（MRI）作为一种成像诊断工具，具有独特的定位，因为它是非侵入性的，不使用辐射。典型的心脏协议依赖于几个MR成像序列来提供不同对比度的图像，下文称为模态，以评估疾病进展和状态。由于这种采集范围，在一次患者检查中生成数百个多维多模态图像，导致严重的数据过载。因此，稳健的自动化分析算法将有助于减轻临床阅读负担。已经提出了几种算法，通过独立考虑它们来分割和配准最常用的模态中的心肌。然而，问题仍然很难解决，业绩还不够好。目前，心脏成像数据的分析仍然是通常由临床专家执行的手动、耗时且昂贵的过程。因此，尽管产生了大量的数据，不仅在临床上，而且在研究环境中，只有一小部分得到了有力的分析，该提案旨在通过提出利用跨模态存在的共享信息的机制来解决上述缺点，以实现心脏成像数据的联合分析，从而使我们的分析方法有了重大的飞跃。我们提出了新的多模态机器学习驱动机制来学习图像特征（即如何表示局部图像信息以供算法使用），这些特征在成像模式之间不会改变，同时保留共享的解剖信息。然后，我们将在基于多模态块的心肌分割和模态间非线性配准（即，来自不同心脏MR序列的两个图像之间的非线性配准）中使用学习到的特征，从而使我们能够将不同模态中同一患者的图像关联起来。为了最大限度地发挥影响力，我们将开发一个跨模态心脏配准插件，用于商业临床包，也可作为学术用途的开源变体提供。我们希望，当我们的完整框架被集成到临床工具，并成为广泛使用，它可以从根本上改变目前的临床阅读工作流程和决策。它将允许跨患者检查的多模态图像轻松无缝地传播注释，从而显著减少阅读时间并允许更大规模地分析心脏数据。

A sedentary lifestyle, poor diet, smoking, and genetic and other health factors are major contributors to coronary heart disease (CHD). Despite recent medical advances that have lowered the number of deaths compared to the past decades, CHD still remains the number 1 disease in mortality in the UK (73,000 deaths per year) with a tremendous economic burden: estimates put the cost to UK's economy at £6.7 billion per year. The overriding goal of this project is to take advantage of multimodal information within cardiac magnetic resonance images to improve their analysis and facilitate the diagnosis and improve treatment of CHD.Magnetic Resonance Imaging (MRI) as an imaging diagnostic tool is uniquely positioned to help as it is non-invasive and does not use radiation. A typical cardiac protocol relies on several MR imaging sequences to provide images of different contrast, termed as modalities hereafter, to assess disease progression and status. As a result of this range of acquisitions, hundreds of multidimensional multimodal images are generated in a single patient exam leading to severe data overload. Therefore, robust and automated analyses algorithms would help alleviate the clinical reading burden. Several algorithms have been proposed to segment and register the myocardium in the most commonly used modalities by considering them independently. However, the problem remains difficult and performance is not yet adequate. Currently, the analysis of cardiac imaging data still remains a manual, time consuming, and expensive process typically performed by clinical experts. As a result, despite the huge amount of data generated, not only in a clinical but also in a research setting, only a fraction is being analysed robustly, due to the vast amount of time required for the analysis of this data.This proposal aims to address the above shortcomings by proposing mechanisms that take advantage of the shared information that exists across modalities to enable the joint analysis of cardiac imaging data and thus make a significant leap in how we approach their analysis. We propose new multimodal machine learning driven mechanisms to learn image features (i.e. how local image information is represented for an algorithm to use) that do not change between imaging modalities whilst preserving shared anatomical information. We will then use the learned features in multimodal patch-based myocardial segmentation and inter-modality non-linear registration (i.e. the non-linear registration between two images coming from different cardiac MR sequences) thus enabling us to relate images of the same patient across different modalities. To maximise impact, we will develop an inter-modality cardiac registration plugin for a commercial clinical package that is also offered as an open source variant for academic purposes. We expect that when our complete framework is integrated into clinical tools and becomes widely available it can radically change current clinical reading workflow and decision-making. It will permit the propagation of annotations across multimodal images of a patient exam effortlessly and seamlessly, thus significantly reducing reading time and permitting the analysis of cardiac data on a larger scale.