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

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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.