85 results found
Biffi C, de Marvao A, Attard MI, et al., 2018, Three-dimensional cardiovascular imaging-genetics: a mass univariate framework, BIOINFORMATICS, Vol: 34, Pages: 97-103, ISSN: 1367-4803
Dawes T, Cai J, Quinlan M, et al., 2018, Fractal analysis of right ventricular trabeculae in pulmonary hypertension, Radiology, ISSN: 0033-8419
Oktay O, Ferrante E, Kamnitsas K, et al., 2018, Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation, Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Whiffin N, Walsh R, Govind R, et al., 2018, CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation., Genet Med
PurposeInternationally adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (http://www.cardioclassifier.org), a semiautomated decision-support tool for inherited cardiac conditions (ICCs).MethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.ResultsWe benchmarked CardioClassifier on 57 expertly curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically actionable variants (64/219 vs. 156/219, Fisher's P = 1.1 × 10-18), with important false positives, illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.ConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible, and interactive variant pathogenicity reports, according to best practice guidelines.GENETICS in MEDICINE advance online publication, 25 January 2018; doi:10.1038/gim.2017.258.
Cai J, Bryant JA, Thu-Thao L, et al., 2017, Fractal analysis of left ventricular trabeculations is associated with impaired myocardial deformation in healthy Chinese, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 19, ISSN: 1097-6647
Dawes T, de Marvao A, Shi W, et al., 2017, Systolic motion of the basal right ventricular freewall is the strongest predictor of global function: a high resolution 3D imaging study, Association-of-Anaesthetists-of-Great-Britain-and-Ireland (AAGBI) GAT Annual Scientific Meeting, Publisher: WILEY, Pages: 77-77, ISSN: 0003-2409
Dawes TJW, de Marvao A, Shi W, et al., 2017, Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study, RADIOLOGY, Vol: 283, Pages: 381-390, ISSN: 0033-8419
Esslinger U, Garnier S, Korniat A, et al., 2017, Exome-wide association study reveals novel susceptibility genes to sporadic dilated cardiomyopathy, PLOS ONE, Vol: 12, ISSN: 1932-6203
Le T-T, Bryant JA, Ting AE, et al., 2017, Assessing exercise cardiac reserve using real-time cardiovascular magnetic resonance, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 19, ISSN: 1097-6647
Michelakis ED, Gurtu V, Webster L, et al., 2017, Inhibition of pyruvate dehydrogenase kinase improves pulmonary arterial hypertension in genetically susceptible patients, SCIENCE TRANSLATIONAL MEDICINE, Vol: 9, ISSN: 1946-6234
Oktay O, Bai W, Guerrero R, et al., 2017, Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 36, Pages: 332-342, ISSN: 0278-0062
Oktay O, Ferrante E, Kamnitsas K, et al., 2017, Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation, IEEE Transactions on Medical Imaging, ISSN: 0278-0062
Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning based techniques. However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac datasets and public benchmarks. Additionally, we demonstrate how the learnt deep models of 3D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.
Pirola S, Cheng Z, Jarral OA, et al., 2017, On the choice of outlet boundary conditions for patient-specific analysis of aortic flow using computational fluid dynamics, JOURNAL OF BIOMECHANICS, Vol: 60, Pages: 15-21, ISSN: 0021-9290
Schafer S, de Marvao A, Adami E, et al., 2017, Titin-truncating variants affect heart function in disease cohorts and the general population, NATURE GENETICS, Vol: 49, Pages: 46-53, ISSN: 1061-4036
Suzuki H, Gao H, Bai W, et al., 2017, Abnormal brain white matter microstructure is associated with both pre-hypertension and hypertension, PLOS ONE, Vol: 12, ISSN: 1932-6203
Tarroni G, Oktay O, Bai W, et al., 2017, Learning-based heart coverage estimation for short-axis cine cardiac MR images, Pages: 73-82, ISSN: 0302-9743
© Springer International Publishing AG 2017. The correct acquisition of short axis (SA) cine cardiac MR image stacks requires the imaging of the full cardiac anatomy between the apex and the mitral valve plane via multiple 2D slices. While in the clinical practice the SA stacks are usually checked qualitatively to ensure full heart coverage, visual inspection can become infeasible for large amounts of imaging data that is routinely acquired, e.g. in population studies such as the UK Biobank (UKBB). Accordingly, we propose a learning-based technique for the fully-automated estimation of the heart coverage for SA image stacks. The technique relies on the identification of cardiac landmarks (i.e. the apex and the mitral valve sides) on two chamber view long axis images and on the comparison of the landmarks’ positions to the volume covered by the SA stack. Landmark detection is performed using a hybrid random forest approach integrating both regression and structured classification models. The technique was applied on 3000 cases from the UKBB and compared to visual assessment. The obtained results (error rate = 2.3%, sens. = 73%, spec. = 90%) indicate that the proposed technique is able to correctly detect the vast majority of the cases with insufficient coverage, suggesting that it could be used as a fully-automated quality control step for CMR SA image stacks.
Cheng Z, Kidher E, Jarral OA, et al., 2016, Assessment of Hemodynamic Conditions in the Aorta Following Root Replacement with Composite Valve-Conduit Graft, ANNALS OF BIOMEDICAL ENGINEERING, Vol: 44, Pages: 1392-1404, ISSN: 0090-6964
Corden B, de Marvao A, Dawes TJ, et al., 2016, Relationship between body composition and left ventricular geometry using three dimensional cardiovascular magnetic resonance, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 18, ISSN: 1097-6647
Dawes T, de Marvao A, Shi W, et al., 2016, Use of artificial intelligence to predict survival in pulmonary hypertension, Spring Meeting on Clinician Scientists in Training, Publisher: ELSEVIER SCIENCE INC, Pages: 35-35, ISSN: 0140-6736
Dawes TJW, Corden B, Cotter S, et al., 2016, Moderate Physical Activity in Healthy Adults Is Associated With Cardiac Remodeling, CIRCULATION-CARDIOVASCULAR IMAGING, Vol: 9, ISSN: 1941-9651
Dawes TJW, Gandhi A, de Marvao A, et al., 2016, Pulmonary Artery Stiffness Is Independently Associated with Right Ventricular Mass and Function: A Cardiac MR Imaging Study, RADIOLOGY, Vol: 280, Pages: 398-404, ISSN: 0033-8419
Durighel G, Tokarczuk PF, Karsa A, et al., 2016, Acute myocardial infarction: susceptibility-weighted cardiac MRI for the detection of reperfusion haemorrhage at 1.5 T, CLINICAL RADIOLOGY, Vol: 71, Pages: E150-E156, ISSN: 0009-9260
Harden SP, Bull RK, Bury RW, et al., 2016, The safe practice of CT coronary angiography in adult patients in UK imaging departments., Clin Radiol, Vol: 71, Pages: 722-728
Computed tomography coronary angiography is increasingly used in imaging departments in the investigation of patients with chest pain and suspected coronary artery disease. Due to the routine use of heart rate controlling medication and the potential for very high radiation doses during these scans, there is a need for guidance on best practice for departments performing this examination, so the patient can be assured of a good quality scan and outcome in a safe environment. This article is a summary of the document on 'Standards of practice of computed tomography coronary angiography (CTCA) in adult patients' published by the Royal College of Radiologists (RCR) in December 2014.
Jaijee S, Quinlan M, Tokarczuk P, et al., 2016, DETERIORATION OF RIGHT VENTRICULAR FUNCTION ON EXERCISE DETECTED BY EXERCISE CARDIAC MAGNETIC RESONANCE IMAGING IN PATIENTS WITH PULMONARY ARTERIAL HYPERTENSION, Annual Conference of the British-Cardiovascular-Society (BCS) on Prediction and Prevention, Publisher: BMJ PUBLISHING GROUP, Pages: A88-A89, ISSN: 1355-6037
Jaijee S, Quinlan M, Tokarczuk P, et al., 2016, Cardiac Magnetic Resonance Imaging In Healthy Volunteers In Normoxic And Hypoxic Exercise, International Conference of the American-Thoracic-Society (ATS), Publisher: AMER THORACIC SOC, ISSN: 1073-449X
Jaijee S, Statton B, Quinlan M, et al., 2016, Right ventricular function in acute and chronic pulmonary hypertension using exercise cardiac magnetic resonance imaging, Congress of the European-Society-of-Cardiology (ESC), Publisher: OXFORD UNIV PRESS, Pages: 1186-1186, ISSN: 0195-668X
O'Regan DP, 2016, Stiff Arteries, Stiff Ventricles: Correlation or Causality in Heart Failure?, Circ Cardiovasc Imaging, Vol: 9
Oktay O, Tarroni G, Bai W, et al., 2016, Respiratory Motion Correction for 2D Cine Cardiac MR Images using Probabilistic Edge Maps, 43rd Computing in Cardiology Conference (CinC), Publisher: IEEE, Pages: 129-132, ISSN: 2325-8861
Schafer S, De Marvao A, Adami E, et al., 2016, Titin truncations cause penetrant cardiac phenotypes in disease and the general population, Congress of the European-Society-of-Cardiology (ESC), Publisher: OXFORD UNIV PRESS, Pages: 1408-1408, ISSN: 0195-668X
de Marvao A, Cook SA, O'Regan DP, 2016, Precursors of Hypertensive Heart Phenotype Develop in Healthy Adults: An Alternative Explanation Reply, JACC-CARDIOVASCULAR IMAGING, Vol: 9, Pages: 763-764, ISSN: 1936-878X
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.