Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Role of Imaging in Left Atrial Appendage Occlusion

Percutaneous left atrial appendage (LAA) occlusion is now a valid alternative to long-term oral anticoagulation in patients with non-valvular atrial fibrillation at high thrombo-embolism risk, especially for patients who are considered ineligible for anticoagulation. The most frequently used occluders worldwide include the WATCHAMN (Boston Scientific, Natick, MA, USA) and the Amplatzer Cardiac Plug or Amulet (St. Jude Medical/Abbott, St Paul, MN, USA) devices. Multimodality imaging is key in the understanding of 3D aspects of the LAA and surrounding structures anatomy. Imaging is essential for procedural planning, during each step of the procedure and for device surveillance after implantation. Multimodality imaging, including 2D/3D echocardiography, fluoroscopy, and cardiac computed tomography can increase the safety and efficacy of the procedure

Three-dimensional reconstruction of myocardial contrast perfusion from biplane cineangiograms by means of linear programming techniques

The assessment of coronary flow reserve from the instantaneous distribution of the contrast agent within the coronary vessels and myocardial muscle at the control state and at maximal flow has been limited by the superimposition of myocardial regions of interest in the two-dimensional images. To overcome these limitations, we are in the process of developing a three-dimensional (3D) reconstruction technique to compute the contrast distribution in cross sections of the myocardial muscle from two orthogonal cineangiograms. To limit the number of feasible solutions in the 3D-reconstruction space, the 3D-geometry of the endo- and epicardial boundaries of the myocardium must be determined. For the geometric reconstruction of the epicardium, the centerlines of the left coronary arterial tree are manually or automatically traced in the biplane views. Next, the bifurcations are detected automatically and matched in these two views, allowing a 3D-representation of the coronary tree. Finally, the circumference of the left ventricular myocardium in a selected cross section can be computed from the intersection points of this cross section with the 3D coronary tree using B-splines. For the geometric reconstruction of the left ventricular cavity, we envision to apply the elliptical approximation technique using the LV boundaries defined in the two orthogonal views, or by applying more complex 3D-reconstruction techniques including densitometry. The actual 3D-reconstruction of the contrast distribution in the myocardium is based on a linear programming technique (Transportation model) using cost coefficient matrices. Such a cost coefficient matrix must contain a maximum amount of a priori information, provided by a computer generated model and updated with actual data from the angiographic views. We have only begun to solve this complex problem. However, based on our first experimental results we expect that the linear programming approach with advanced cost coefficient matrices and computed model will lead to a

Coronary Artery Segmentation and Motion Modelling

Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated

Dysfunctional but viable myocardium - ischemic heart disease assessed by magnetic resonance imaging and single photon emission computed tomography

The assessment of ischemic heart disease (IHD) often focuses on the detection of dysfunctional but viable myocardium which may improve in function following revascularization. Dysfunctional but viable myocardium is identified by distinct characteristics with regards to function, perfusion and viability. Therefore, in Paper I we developed a method for quantitative polar representation of left ventricular myocardial function, perfusion and viability using single photon emission computed tomography (SPECT) and cardiac magnetic resonance (CMR). Polar representation of these parameters was feasible, and the quantitative method agreed with visual assessment. Paper II showed that wall thickening decreases with increasing infarct transmurality. However, the variation in wall thickening was large, and importantly, influenced more so by the function of adjacent myocardium than by infarct transmurality. This underscores the difficulty of using resting function alone to accurately assess myocardial infarction in revascularized IHD. In Paper III we assessed the relationship between left ventricular ejection fraction (LVEF) and infarct size and found that LVEF cannot be used to estimate infarct size, and vice versa. However, the study showed that LVEF can be used to estimate a maximum predicted infarct size, and that infarct size can be used to estimate a maximum predicted LVEF. These results emphasize the importance of direct infarct imaging by CMR when attempting to estimate the size of infarction in patients with IHD. Paper IV was designed to assess the time course of recovery of myocardial perfusion and function after revascularization. The recovery of perfusion was found to occur in the first month, while the recovery of function was delayed in segments with non-transmural infarction. In summary, the presented studies emphasize the importance of direct infarct imaging by CMR for the accurate identification of infarction in the assessment of dysfunctional myocardium. Neither regional nor global myocardial function have a close correlation to infarction, but the presence of non-transmural infarction is a marker for delayed recovery of function following revascularization

Analysis of cardiac motion using MRI and nonrigid image registration

Imperial Users onl

Multi-modality cardiac image computing: a survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future

Analysis of first pass myocardial perfusion imaging with magnetic resonance

Early diagnosis and localisation of myocardial perfusion defects is an important step in the treatment of coronary artery disease. Thus far, coronary angiography is the conventional standard investigation for patients with known or suspected coronary artery disease and it provides information about the presence and location of coronary stenoses. In recent years, the development of myocardial perfusion CMR has extended the role of MR in the evaluation of ischaemic heart disease beyond the situations where there have already been gross myocardial changes such as acute infarction or scarring. The ability to non-invasively evaluate cardiac perfusion abnormalities before pathologic effects occur, or as follow-up to therapy, is important to the management of patients with coronary artery disease. Whilst limited multi-slice 2D CMR perfusion studies are gaining increased clinical usage for quantifying gross ischaemic burden, research is now directed towards complete 3D coverage of the myocardium for accurate localisation of the extent of possible defects. In 3D myocardial perfusion imaging, a complete volumetric data set has to be acquired for each cardiac cycle in order to study the first pass of the contrast bolus. This normally requires a relatively large acquisition window within each cardiac cycle to ensure a comprehensive coverage of the myocardium and reasonably high resolution of the images. With multi-slice imaging, long axis cardiac motion during this large acquisition window can cause the myocardium imaged in different cross- sections to be mis-registered, i.e., some part of the myocardium may be imaged more than twice whereas other parts may be missed out completely. This type of mis-registration is difficult to correct for by using post-processing techniques. The purpose of this thesis is to investigate techniques for tracking through plane motion during 3D myocardial perfusion imaging, and a novel technique for extracting intrinsic relationships between 3D cardiac deformation due to respiration and multiple ID real-time measurable surface intensity traces is developed. Despite the fact that these surface intensity traces can be strongly coupled with each other but poorly correlated with respiratory induced cardiac deformation, we demonstrate how they can be used to accurately predict cardiac motion through the extraction of latent variables of both the input and output of the model. The proposed method allows cross-modality reconstruction of patient specific models for dense motion field prediction, which after initial modelling can be use in real-time prospective motion tracking or correction. In CMR, new imaging sequences have significantly reduced the acquisition window whilst maintaining the desired spatial resolution. Further improvements in perfusion imaging will require the application of parallel imaging techniques or making full use of the information content of the ¿-space data. With this thesis, we have proposed RR-UNFOLD and RR-RIGR for significantly reducing the amount of data that is required to reconstruct the perfusion image series. The methods use prospective diaphragmatic navigator echoes to ensure UNFOLD and RIGR are carried out on a series of images that are spatially registered. An adaptive real-time re-binning algorithm is developed for the creation of static image sub-series related to different levels of respiratory motion. Issues concerning temporal smoothing of tracer kinetic signals and residual motion artefact are discussed, and we have provided a critical comparison of the relative merit and potential pitfalls of the two techniques. In addition to the technical and theoretical descriptions of the new methods developed, we have also provided in this thesis a detailed literature review of the current state-of-the-art in myocardial perfusion imaging and some of the key technical challenges involved. Issues concerning the basic background of myocardial ischaemia and its functional significance are discussed. Practical solutions to motion tracking during imaging, predictive motion modelling, tracer kinetic modelling, RR-UNFOLD and RR-RIGR are discussed, all with validation using patient and normal subject data to demonstrate both the strength and potential clinical value of the proposed techniques.Open acces

Translating computational modelling tools for clinical practice in congenital heart disease

Increasingly large numbers of medical centres worldwide are equipped with the means to acquire 3D images of patients by utilising magnetic resonance (MR) or computed tomography (CT) scanners. The interpretation of patient 3D image data has significant implications on clinical decision-making and treatment planning. In their raw form, MR and CT images have become critical in routine practice. However, in congenital heart disease (CHD), lesions are often anatomically and physiologically complex. In many cases, 3D imaging alone can fail to provide conclusive information for the clinical team. In the past 20-30 years, several image-derived modelling applications have shown major advancements. Tools such as computational fluid dynamics (CFD) and virtual reality (VR) have successfully demonstrated valuable uses in the management of CHD. However, due to current software limitations, these applications have remained largely isolated to research settings, and have yet to become part of clinical practice. The overall aim of this project was to explore new routes for making conventional computational modelling software more accessible for CHD clinics. The first objective was to create an automatic and fast pipeline for performing vascular CFD simulations. By leveraging machine learning, a solution was built using synthetically generated aortic anatomies, and was seen to be able to predict 3D aortic pressure and velocity flow fields with comparable accuracy to conventional CFD. The second objective was to design a virtual reality (VR) application tailored for supporting the surgical planning and teaching of CHD. The solution was a Unity-based application which included numerous specialised tools, such as mesh-editing features and online networking for group learning. Overall, the outcomes of this ongoing project showed strong indications that the integration of VR and CFD into clinical settings is possible, and has potential for extending 3D imaging and supporting the diagnosis, management and teaching of CHD