389 research outputs found

    Multi-modality cardiac image computing: a survey

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    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

    Aortic Stenosis: Multimorbidity and Myocardial Impact on Patients undergoing Transcatheter Aortic Valve Implantation

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    Introduction: In aortic stenosis (AS), the myocardium remodels to compensate for the obstruction to forward flow before eventually decompensating, often acutely- termed acute decompensated AS (ADAS). Patients with AS often have other comorbidities, including coronary artery disease (CAD), cardiac amyloidosis (ATTR) and frailty which may also influence the myocardium and outcomes. This thesis examines the impact of multimorbidity on the myocardium and outcomes, diagnostic markers and decompensation in three patient populations: ATTR, CAD and ADAS. / Methods: To evaluate the impact of AS and ATTR on the combined phenotype AS-ATTR, I compared 4 prospective cohorts (n=583): elderly controls, severe AS, AS-ATTR and ATTR. Using a single-centre, registry I retrospectively evaluated the impact o mong 1902 transcatheter aortic valve implantation (TAVI) patients, I assessed the impact of CAD stratified by location (left main stem (LMS) vs non-LMS) and territory (single-vessel vs multi-vessel) on mortality. I examined the diagnostic ability of 3 commonly used metrics: Troponin T, ischaemic ECG and angina, to diagnose a type 1 NSTEMI in 273 AS patients with acute presentations. I compared outcomes with TAVI in patients with ADAS vs non-ADAS. Within the ADAS cohort, I evaluated the prognostic role of a new echo based staging classification. / Results: Dual pathology with AS-ATTR is more closely related to ATTR than it is to AS, despite a similar burden of amyloid. Only LMS CAD was independently associated with mortality (HR: 1.57) after the first year post-TAVI. All 3 metrics have a low sensitivity and diagnostic ability (AUC 0.625, 0.559 and 0.692 respectively). TAVI procedural complications and mortality were similar between ADAS and non-ADAS cohorts. However, ADAS independently predicted mortality at 30 days (HR 1.02). Among ADAS patients, advanced cardiac damage/dysfunction predicts mortality at 1 year (HR 1.853) whilst frailty predicts mortality at 2.4 years (HR 1.667). / Conclusions: This thesis has demonstrated the effect of dual pathology (AS-ATTR) on altering the resultant AS phenotype, the prognostic impact of multimorbidity (frailty and LMS CAD) in TAVI, the impact of AS on confounding common diagnostic pathways (NSTEMI) and identified a novel prognostic marker (ADAS)

    Pacing with restoration of respiratory sinus arrhythmia improved cardiac contractility and the left ventricular output: a translational study

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    Introduction: Respiratory sinus arrhythmia (RSA) is a prognostic value for patients with heart failure and is defined as a beat-to-beat variation of the timing between the heart beats. Patients with heart failure or patients with permanent cardiac pacing might benefit from restoration of RSA. The aim of this translational, proof-of-principle study was to evaluate the effect of pacing with or without restored RSAon parameters of LV cardiac contractility and the cardiac output

    Towards Patient Specific Mitral Valve Modelling via Dynamic 3D Transesophageal Echocardiography

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    Mitral valve disease is a common pathologic problem occurring increasingly in an aging population, and many patients suffering from mitral valve disease require surgical intervention. Planning an interventional approach from diagnostic imaging alone remains a significant clinical challenge. Transesophageal echocardiography (TEE) is the primary imaging modality used diagnostically, it has limitations in image quality and field-of-view. Recently, developments have been made towards modelling patient-specific deformable mitral valves from TEE imaging, however, a major barrier to producing accurate valve models is the need to derive the leaflet geometry through segmentation of diagnostic TEE imaging. This work explores the development of volume compounding and automated image analysis to more accurately and quickly capture the relevant valve geometry needed to produce patient-specific mitral valve models. Volume compounding enables multiple ultrasound acquisitions from different orientations and locations to be aligned and blended to form a single volume with improved resolution and field-of-view. A series of overlapping transgastric views are acquired that are then registered together with the standard en-face image and are combined using a blending function. The resulting compounded ultrasound volumes allow the visualization of a wider range of anatomical features within the left heart, enhancing the capabilities of a standard TEE probe. In this thesis, I first describe a semi-automatic segmentation algorithm based on active contours designed to produce segmentations from end-diastole suitable for deriving 3D printable molds. Subsequently I describe the development of DeepMitral, a fully automatic segmentation pipeline which leverages deep learning to produce very accurate segmentations with a runtime of less than ten seconds. DeepMitral is the first reported method using convolutional neural networks (CNNs) on 3D TEE for mitral valve segmentations. The results demonstrate very accurate leaflet segmentations, and a reduction in the time and complexity to produce a patient-specific mitral valve replica. Finally, a real-time annulus tracking system using CNNs to predict the annulus coordinates in the spatial frequency domain was developed. This method facilitates the use of mitral annulus tracking in real-time guidance systems, and further simplifies mitral valve modelling through the automatic detection of the annulus, which is a key structure for valve quantification, and reproducing accurate leaflet dynamics

    Fighting against atherosclerotic disease: From the endothelium to invasive cardiology

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    An insight into in vitro strategies to improve endothelial function and response to ischemia and into clinical strategies to improve the outcome after percutaneous interventions

    Optimizing Outcomes of Aortic Valve Replacement

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    Surgical Aortic Valve Replacement In the Era of Transcatheter Aortic Valve Replacement

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