669 results found
Dawes TJW, de Marvao A, Shi W, et al., 2019, Identifying the optimal regional predictor of right ventricular global function: a high-resolution three-dimensional cardiac magnetic resonance study., Anaesthesia, Vol: 74, Pages: 312-320
Right ventricular (RV) function has prognostic value in acute, chronic and peri-operative disease, although the complex RV contractile pattern makes rapid assessment difficult. Several two-dimensional (2D) regional measures estimate RV function, however the optimal measure is not known. High-resolution three-dimensional (3D) cardiac magnetic resonance cine imaging was acquired in 300 healthy volunteers and a computational model of RV motion created. Points where regional function was significantly associated with global function were identified and a 2D, optimised single-point marker (SPM-O) of global function developed. This marker was prospectively compared with tricuspid annular plane systolic excursion (TAPSE), septum-freewall displacement (SFD) and their fractional change (TAPSE-F, SFD-F) in a test cohort of 300 patients in the prediction of RV ejection fraction. RV ejection fraction was significantly associated with systolic function in a contiguous 7.3 cm2 patch of the basal RV freewall combining transverse (38%), longitudinal (35%) and circumferential (27%) contraction and coinciding with the four-chamber view. In the test cohort, all single-point surrogates correlated with RV ejection fraction (p < 0.010), but correlation (R) was higher for SPM-O (R = 0.44, p < 0.001) than TAPSE (R = 0.24, p < 0.001) and SFD (R = 0.22, p < 0.001), and non-significantly higher than TAPSE-F (R = 0.40, p < 0.001) and SFD-F (R = 0.43, p < 0.001). SPM-O explained more of the observed variance in RV ejection fraction (19%) and predicted it more accurately than any other 2D marker (median error 2.8 ml vs 3.6 ml, p < 0.001). We conclude that systolic motion of the basal RV freewall predicts global function more accurately than other 2D estimators. However, no markers summarise 3D contractile patterns, limiting their predictive accuracy.
Bastiani M, Andersson JLR, Cordero-Grande L, et al., 2019, Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project, NEUROIMAGE, Vol: 185, Pages: 750-763, ISSN: 1053-8119
Bello G, Dawes T, Duan J, et al., Deep learning cardiac motion analysis for human survival prediction, Nature Machine Intelligence, ISSN: 2522-5839
Qin C, Schlemper J, Caballero J, et al., 2019, Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 38, Pages: 280-290, ISSN: 0278-0062
Balaban G, Halliday BP, Costa CM, et al., 2018, Fibrosis Microstructure Modulates Reentry in Non-ischemic Dilated Cardiomyopathy: Insights From Imaged Guided 2D Computational Modeling, FRONTIERS IN PHYSIOLOGY, Vol: 9, ISSN: 1664-042X
Attard MI, Dawes TJW, de Marvao A, et al., 2018, Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: 3D analysis of cardiac magnetic resonance imaging., Eur Heart J Cardiovasc Imaging
Aims: We sought to identify metabolic pathways associated with right ventricular (RV) adaptation to pulmonary hypertension (PH). We evaluated candidate metabolites, previously associated with survival in pulmonary arterial hypertension, and used automated image segmentation and parametric mapping to model their relationship to adverse patterns of remodelling and wall stress. Methods and results: In 312 PH subjects (47.1% female, mean age 60.8 ± 15.9 years), of which 182 (50.5% female, mean age 58.6 ± 16.8 years) had metabolomics, we modelled the relationship between the RV phenotype, haemodynamic state, and metabolite levels. Atlas-based segmentation and co-registration of cardiac magnetic resonance imaging was used to create a quantitative 3D model of RV geometry and function-including maps of regional wall stress. Increasing mean pulmonary artery pressure was associated with hypertrophy of the basal free wall (β = 0.29) and reduced relative wall thickness (β = -0.38), indicative of eccentric remodelling. Wall stress was an independent predictor of all-cause mortality (hazard ratio = 1.27, P = 0.04). Six metabolites were significantly associated with elevated wall stress (β = 0.28-0.34) including increased levels of tRNA-specific modified nucleosides and fatty acid acylcarnitines, and decreased levels (β = -0.40) of sulfated androgen. Conclusion: Using computational image phenotyping, we identify metabolic profiles, reporting on energy metabolism and cellular stress-response, which are associated with adaptive RV mechanisms to PH.
Tarroni G, Oktay O, Bai W, et al., 2018, Learning-Based Quality Control for Cardiac MR Images., IEEE Trans Med Imaging
The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artefacts such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images: however, this procedure is strongly operatordependent, cumbersome and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully-automated, learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation, 2) inter-slice motion detection, 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method - integrating both regression and structured classification models - to extract landmarks as well as probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank as well as on 100 cases from the UK Digital Heart Project, and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition.
Bozek J, Makropoulos A, Schuh A, et al., 2018, Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project, NEUROIMAGE, Vol: 179, Pages: 11-29, ISSN: 1053-8119
Bai W, Sinclair M, Tarroni G, et al., 2018, Automated cardiovascular magnetic resonance image analysis with fully convolutional networks, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 20, ISSN: 1097-6647
Hou B, Khanal B, Alansary A, et al., 2018, 3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 37, Pages: 1737-1750, ISSN: 0278-0062
Parisot S, Ktena SI, Ferrante E, et al., 2018, Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease, MEDICAL IMAGE ANALYSIS, Vol: 48, Pages: 117-130, ISSN: 1361-8415
Chen L, Jones ALC, Mair G, et al., 2018, Rapid Automated Quantification of Cerebral Leukoaraiosis on CT Images: A Multicenter Validation Study, RADIOLOGY, Vol: 288, Pages: 573-581, ISSN: 0033-8419
Ledig C, Schuh A, Guerrero R, et al., 2018, Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database, SCIENTIFIC REPORTS, Vol: 8, ISSN: 2045-2322
Dawes T, Serrani M, Bai W, et al., 2018, Myocardial trabeculae improve left ventricular function: a combined UK Biobank and computational analysis, AAGBI GAT Annual Scientific Meeting, Publisher: WILEY, Pages: 12-12, ISSN: 0003-2409
Koch LM, Rajchl M, Bai W, et al., 2018, Multi-Atlas Segmentation Using Partially Annotated Data: Methods and Annotation Strategies, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol: 40, Pages: 1683-1696, ISSN: 0162-8828
Cerrolaza JJ, Li Y, Biffi C, et al., 2018, Fetal Skull Reconstruction via Deep Convolutional Autoencoders., Conf Proc IEEE Eng Med Biol Soc, Vol: 2018, Pages: 887-890, ISSN: 1557-170X
Ultrasound (US) imaging is arguably the most commonly used modality for fetal screening. Recently, 3DUS has been progressively adopted in modern obstetric practice, showing promising diagnosis capabilities, and alleviating many of the inherent limitations of traditional 2DUS, such as subjectivity and operator dependence. However, the involuntary movements of the fetus, and the difficulty for the operator to inspect the entire volume in real-time can hinder the acquisition of the entire region of interest. In this paper, we present two deep convolutional architectures for the reconstruction of the fetal skull in partially occluded 3DUS volumes: a TL deep convolutional network (TL-Net), and a conditional variational autoencoder (CVAE). The performance of the two networks was evaluated for occlusion rates up to 50%, both showing accurate results even when only 60% of the skull is included in the US volume (Dice coeff. $0.84\pm 0.04$ for CVAE and $0.83\pm 0.03$ for TL-Net). The reconstruction networks proposed here have the potential to optimize image acquisition protocols in obstetric sonography, reducing the acquisition time and providing comprehensive anatomical information even from partially occluded images.
Sinclair M, Baumgartner CF, Matthew J, et al., 2018, Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks., Conf Proc IEEE Eng Med Biol Soc, Vol: 2018, Pages: 714-717, ISSN: 1557-170X
Measurement of head biometrics from fetal ultrasonography images is of key importance in monitoring the healthy development of fetuses. However, the accurate measurement of relevant anatomical structures is subject to large inter-observer variability in the clinic. To address this issue, an automated method utilizing Fully Convolutional Networks (FCN) is proposed to determine measurements of fetal head circumference (HC) and biparietal diameter (BPD). An FCN was trained on approximately 2000 2D ultrasound images of the head with annotations provided by 45 different sonographers during routine screening examinations to perform semantic segmentation of the head. An ellipse is fitted to the resulting segmentation contours to mimic the annotation typically produced by a sonographer. The model's performance was compared with inter-observer variability, where two experts manually annotated 100 test images. Mean absolute model-expert error was slightly better than inter-observer error for HC (1.99mm vs 2.16mm), and comparable for BPD (0.61mm vs 0.59mm), as well as Dice coefficient (0.980 vs 0.980). Our results demonstrate that the model performs at a level similar to a human expert, and learns to produce accurate predictions from a large dataset annotated by many sonographers. Additionally, measurements are generated in near real-time at 15fps on a GPU, which could speed up clinical workflow for both skilled and trainee sonographers.
Makropoulos A, Robinson EC, Schuh A, et al., 2018, The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction, NEUROIMAGE, Vol: 173, Pages: 88-112, ISSN: 1053-8119
Bruun M, Rhodius-Meester H, Baroni M, et al., 2018, Biomarkers in differential diagnosis of dementia using a data-driven approach, 4th Congress of the European-Academy-of-Neurology (EAN), Publisher: WILEY, Pages: 278-278, ISSN: 1351-5101
de Marvao A, Biffi C, Walsh R, et al., 2018, DEFINING THE EFFECTS OF GENETIC VARIATION USING MACHINE LEARNING ANALYSIS OF CMRS: A STUDY IN HYPERTROPHIC CARDIOMYOPATHY AND IN A HEALTHY POPULATION, Joint Meeting of the British-Society-of-Cardiovascular-Imaging/British-Society-of-Cardiovascular-CT, British-Society-of-Cardiovascular-Magnetic-Resonance and British-Nuclear-Cardiac-Society on British Cardiovascular Imaging, Publisher: BMJ PUBLISHING GROUP, Pages: A7-A8, ISSN: 1355-6037
Oksuz I, Ruijsink B, Puyol-Anton E, et al., 2018, AUTOMATIC MIS-TRIGGERING ARTEFACT DETECTION FOR IMAGE QUALITY ASSESSMENT OF CARDIAC MRI, Joint Meeting of the British-Society-of-Cardiovascular-Imaging/British-Society-of-Cardiovascular-CT, British-Society-of-Cardiovascular-Magnetic-Resonance and British-Nuclear-Cardiac-Society on British Cardiovascular Imaging, Publisher: BMJ PUBLISHING GROUP, Pages: A5-A5, ISSN: 1355-6037
Qin C, Guerrero R, Bowles C, et al., 2018, A large margin algorithm for automated segmentation of white matter hyperintensity, PATTERN RECOGNITION, Vol: 77, Pages: 150-159, ISSN: 0031-3203
Tolonen A, Rhodius-Meester HFM, Bruun M, et al., 2018, Data-Driven Differential Diagnosis of Dementia Using Multiclass Disease State Index Classifier, FRONTIERS IN AGING NEUROSCIENCE, Vol: 10, ISSN: 1663-4365
Makropoulos A, Counsell SJ, Rueckert D, 2018, A review on automatic fetal and neonatal brain MRI segmentation, NEUROIMAGE, Vol: 170, Pages: 231-248, ISSN: 1053-8119
Arslan S, Ktena SI, Makropoulos A, et al., 2018, Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex, NEUROIMAGE, Vol: 170, Pages: 5-30, ISSN: 1053-8119
Rueckert D, 2018, Machine learning for image segmentation, 37th Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO), Publisher: ELSEVIER IRELAND LTD, Pages: S183-S183, ISSN: 0167-8140
Ktena SI, Parisot S, Ferrante E, et al., 2018, Metric learning with spectral graph convolutions on brain connectivity networks, NEUROIMAGE, Vol: 169, Pages: 431-442, ISSN: 1053-8119
Huizinga W, Poot DHJ, Vernooij MW, et al., 2018, A spatio-temporal reference model of the aging brain, NEUROIMAGE, Vol: 169, Pages: 11-22, ISSN: 1053-8119
Garcia KE, Robinson EC, Alexopoulos D, et al., 2018, Dynamic patterns of cortical expansion during folding of the preterm human brain, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 115, Pages: 3156-3161, ISSN: 0027-8424
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