81 results found
Biffi C, de Marvao A, Attard MI, et al., 2017, Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework., Bioinformatics
Motivation: Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for highthroughput mapping of genotype-phenotype associations in three dimensions (3D). Results: High-resolution cardiac magnetic resonance images were automatically segmented in 1,124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts. Availability: The proposed approach has been coded in an R package freely available at https://doi.org/10.5281/zenodo.834610 together with the clinical data used in this work. Supplementary information: Supplementary data are available at Bioinformatics online.
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
OBJECTIVES: To characterize effects of chronically elevated blood pressure on the brain, we tested for brain white matter microstructural differences associated with normotension, pre-hypertension and hypertension in recently available brain magnetic resonance imaging data from 4659 participants without known neurological or psychiatric disease (62.3±7.4 yrs, 47.0% male) in UK Biobank. METHODS: For assessment of white matter microstructure, we used measures derived from neurite orientation dispersion and density imaging (NODDI) including the intracellular volume fraction (an estimate of neurite density) and isotropic volume fraction (an index of the relative extra-cellular water diffusion). To estimate differences associated specifically with blood pressure, we applied propensity score matching based on age, sex, educational level, body mass index, and history of smoking, diabetes mellitus and cardiovascular disease to perform separate contrasts of non-hypertensive (normotensive or pre-hypertensive, N = 2332) and hypertensive (N = 2337) individuals and of normotensive (N = 741) and pre-hypertensive (N = 1581) individuals (p<0.05 after Bonferroni correction). RESULTS: The brain white matter intracellular volume fraction was significantly lower, and isotropic volume fraction was higher in hypertensive relative to non-hypertensive individuals (N = 1559, each). The white matter isotropic volume fraction also was higher in pre-hypertensive than in normotensive individuals (N = 694, each) in the right superior longitudinal fasciculus and the right superior thalamic radiation, where the lower intracellular volume fraction was observed in the hypertensives relative to the non-hypertensive group. SIGNIFICANCE: Pathological processes associated with chronically elevated blood pressure are associated with imaging differences suggesting chronic alterations of white matter axonal structure that may affect cognitive functions even with pre-hypertension.
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
de Marvao A, Meyer H, Dawes T, et al., 2016, Development of integrated high-resolution three-dimensional MRI and computational modelling techniques to identify novel genetic and anthropometric determinants of cardiac form and function, Spring Meeting on Clinician Scientists in Training, Publisher: ELSEVIER SCIENCE INC, Pages: 36-36, ISSN: 0140-6736
Bai W, Peressutti D, Oktay O, et al., 2015, Learning a Global Descriptor of Cardiac Motion from a Large Cohort of 1000+Normal Subjects, 8th International Conference on Functional Imaging and Modeling of the Heart(FIMH), Publisher: SPRINGER-VERLAG BERLIN, Pages: 1-9, ISSN: 0302-9743
Bai W, Shi W, de Marvao A, et al., 2015, A bi-ventricular cardiac atlas built from 1000+high resolution MR images of healthy subjects and an analysis of shape and motion, MEDICAL IMAGE ANALYSIS, Vol: 26, Pages: 133-145, ISSN: 1361-8415
Buyandelger B, Mansfield C, Kostin S, et al., 2015, ZBTB17 (MIZ1) Is Important for the Cardiac Stress Response and a Novel Candidate Gene for Cardiomyopathy and Heart Failure, CIRCULATION-CARDIOVASCULAR GENETICS, Vol: 8, Pages: 643-652, ISSN: 1942-325X
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