The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions
暂无分享,去创建一个
Alejandro F. Frangi | Paul M. Matthews | Steffen E. Petersen | Karla L. Miller | Paul Leeson | Fidel Alfaro-Almagro | Niels Oesingmann | Alejandro F. Frangi | Stefan Neubauer | Stephen M. Smith | C. Sudlow | N. Allen | P. Matthews | R. Collins | T. Littlejohns | K. Miller | S. Petersen | Steve Garratt | N. Oesingmann | Stephen M. Smith | S. Neubauer | Nicola Doherty | F. Alfaro-Almagro | P. Leeson | J. Sellors | N. Crabtree | N. Harvey | Julie Dee Bell | Simon Sheard | J. Holliday | Rory Collins | Steve Garratt | Thomas J. Littlejohns | Jo Holliday | Lorna M. Gibson | Jimmy D. Bell | Chris Boultwood | Megan C. Conroy | Nicola Crabtree | Nicola Doherty | Nicholas C. Harvey | Jonathan Sellors | Simon Sheard | Cathie L. M. Sudlow | Naomi E. Allen | L. Gibson | J. Bell | C. Boultwood | N. Harvey | Jo Holliday | Paul M. Matthews | Joanne Holliday | Nicholas C Harvey | N. Doherty | N. C. Harvey
[1] J. Marchini,et al. Handedness, language areas and neuropsychiatric diseases: insights from brain imaging and genetics , 2019, Brain : a journal of neurology.
[2] P. Harst,et al. Effect of Systolic Blood Pressure on Left Ventricular Structure and Function. , 2019, Hypertension.
[3] A. Young,et al. Changes in Cardiac Morphology and Function in Individuals With Diabetes Mellitus , 2019, Circulation. Cardiovascular imaging.
[4] J. Sellors,et al. The advantages of UK Biobank's open‐access strategy for health research , 2019, Journal of internal medicine.
[5] Euan A. Ashley,et al. Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences , 2019, Nature Communications.
[6] Alejandro F. Frangi,et al. Quantitative CMR population imaging on 20, 000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation , 2019, Medical Image Anal..
[7] J. Pell,et al. Association between APOE e4 and white matter hyperintensity volume, but not total brain volume or white matter integrity , 2019, Brain Imaging and Behavior.
[8] Konrad Werys,et al. Automated localization and quality control of the aorta in cine CMR can significantly accelerate processing of the UK Biobank population data , 2019, PloS one.
[9] S. Petersen,et al. Physical activity and left ventricular trabeculation in the UK Biobank community-based cohort study , 2019, Heart.
[10] Mark Hamer,et al. Association of body mass index and waist-to-hip ratio with brain structure , 2019, Neurology.
[11] C. Sudlow,et al. Genetic variation in PLEKHG1 is associated with white matter hyperintensities (n = 11,226) , 2019, Neurology.
[12] Mark E Bastin,et al. Associations between vascular risk factors and brain MRI indices in UK Biobank , 2019, bioRxiv.
[13] Alejandro F. Frangi,et al. Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging With Fisher-Discriminative 3-D CNN , 2018, IEEE Transactions on Biomedical Engineering.
[14] Cathie Sudlow,et al. UK Biobank: opportunities for cardiovascular research , 2017, European heart journal.
[15] H. Lamb,et al. Erratum: Obesity, brain volume, and white matter microstructure at MRI: A cross-sectional UK biobank study (Radiology (2019) 291 (763-771) DOI: 10.1148/radiol.2019181012) , 2019 .
[16] Jimmy D Bell,et al. Measurement of liver iron by magnetic resonance imaging in the UK Biobank population , 2018, PloS one.
[17] P. Munroe,et al. Association Between Ambient Air Pollution and Cardiac Morpho-Functional Phenotypes , 2018, Circulation.
[18] P. Donnelly,et al. The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.
[19] J. Marchini,et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank , 2018, Nature.
[20] Tian Ge,et al. The Shared Genetic Basis of Educational Attainment and Cerebral Cortical Morphology. , 2018, Cerebral cortex.
[21] Chloe Hutton,et al. Validation of a standardized MRI method for liver fat and T2* quantification , 2018, PloS one.
[22] S. Yang,et al. Bone mineral density assessment for research purpose using dual energy X-ray absorptiometry , 2018, Osteoporosis and sarcopenia.
[23] Aron K Barbey,et al. Small sample sizes reduce the replicability of task-based fMRI studies , 2018, Communications Biology.
[24] Magnus Borga,et al. Body Composition Profiling in the UK Biobank Imaging Study , 2018, Obesity.
[25] M. Dichgans,et al. Genetic Study of White Matter Integrity in UK Biobank (N=8448) and the Overlap With Stroke, Depression, and Dementia , 2018, Stroke.
[26] Magnus Borga,et al. Advanced body composition assessment: from body mass index to body composition profiling , 2018, Journal of Investigative Medicine.
[27] S. Petersen,et al. Variation in lung function and alterations in cardiac structure and function—Analysis of the UK Biobank cardiovascular magnetic resonance imaging substudy , 2018, PloS one.
[28] S. Petersen,et al. The impact of menopausal hormone therapy (MHT) on cardiac structure and function: Insights from the UK Biobank imaging enhancement study , 2018, PloS one.
[29] Jonathan W. Waks,et al. Global ECG Measures and Cardiac Structure and Function: The ARIC Study (Atherosclerosis Risk in Communities). , 2018, Circulation. Arrhythmia and electrophysiology.
[30] M. Delgado-Rodríguez,et al. Systematic review and meta-analysis. , 2017, Medicina intensiva.
[31] Ben Glocker,et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks , 2017, Journal of Cardiovascular Magnetic Resonance.
[32] Ludovica Griffanti,et al. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank , 2017, NeuroImage.
[33] C. Sudlow,et al. Impact of detecting potentially serious incidental findings during multi-modal imaging , 2018, Wellcome open research.
[34] Quincy M. Samus,et al. Dementia prevention, intervention, and care , 2017, The Lancet.
[35] Ben Glocker,et al. Abnormal brain white matter microstructure is associated with both pre-hypertension and hypertension , 2017, PloS one.
[36] C. Denson. The MESA Study. , 2017 .
[37] Jackie A Cooper,et al. The impact of cardiovascular risk factors on cardiac structure and function: Insights from the UK Biobank imaging enhancement study , 2017, PloS one.
[38] C. Sudlow,et al. Protocol and quality assurance for carotid imaging in 100,000 participants of UK Biobank: development and assessment , 2017, European journal of preventive cardiology.
[39] Carmel Hayes,et al. Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results , 2017, The International Journal of Cardiovascular Imaging.
[40] I. Deary,et al. Do regional brain volumes and major depressive disorder share genetic architecture? A study of Generation Scotland (n=19 762), UK Biobank (n=24 048) and the English Longitudinal Study of Ageing (n=5766) , 2017, Translational Psychiatry.
[41] C. Sudlow,et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population , 2017, American journal of epidemiology.
[42] Saifeng Liu,et al. Susceptibility‐weighted imaging: current status and future directions , 2017, NMR in biomedicine.
[43] Stefan Neubauer,et al. Characterisation of liver fat in the UK Biobank cohort , 2017, PloS one.
[44] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[45] Stefan K. Piechnik,et al. Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort , 2017, Journal of Cardiovascular Magnetic Resonance.
[46] Sebastian Thrun,et al. Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.
[47] Subhashini Venugopalan,et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.
[48] H. Whalley,et al. Association of polygenic risk for major psychiatric illness with subcortical volumes and white matter integrity in UK Biobank , 2016, Scientific Reports.
[49] Magnus Borga,et al. Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies , 2016, PloS one.
[50] P. Matthews,et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.
[51] H. Tilg,et al. Nonalcoholic fatty liver disease and hepatocellular carcinoma. , 2016, Metabolism: Clinical and Experimental.
[52] John Eng,et al. Adverse Left Ventricular Remodeling and Age Assessed with Cardiac MR Imaging: The Multi-Ethnic Study of Atherosclerosis. , 2016, Radiology.
[53] A. Hofman,et al. The Rotterdam Scan Study: design update 2016 and main findings , 2015, European Journal of Epidemiology.
[54] P. Matthews,et al. UK Biobank’s cardiovascular magnetic resonance protocol , 2015, Journal of Cardiovascular Magnetic Resonance.
[55] J. Linseisen,et al. Whole-Body MR Imaging in the German National Cohort: Rationale, Design, and Technical Background. , 2015, Radiology.
[56] Jennifer S Gregory,et al. Reproducibility and Diagnostic Accuracy of Kellgren-Lawrence Grading for Osteoarthritis Using Radiographs and Dual-Energy X-ray Absorptiometry Images. , 2015, Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry.
[57] P. Elliott,et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.
[58] C. Morrison,et al. Hormonal Contraception and the Risk of HIV Acquisition: An Individual Participant Data Meta-analysis , 2015, PLoS medicine.
[59] D. Bluemke,et al. Proximal aortic distensibility is an independent predictor of all-cause mortality and incident CV events: the MESA study. , 2014, Journal of the American College of Cardiology.
[60] M. Robson,et al. Multiparametric magnetic resonance for the non-invasive diagnosis of liver disease , 2014, Journal of hepatology.
[61] Whal Lee. General principles of carotid Doppler ultrasonography , 2013, Ultrasonography.
[62] P. Matthews,et al. Osteoporosis epidemiology in UK Biobank: a unique opportunity for international researchers , 2013, Osteoporosis International.
[63] Udo Hoffmann,et al. Body fat distribution, incident cardiovascular disease, cancer, and all-cause mortality. , 2013, Journal of the American College of Cardiology.
[64] P. Matthews,et al. Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches , 2013, Journal of Cardiovascular Magnetic Resonance.
[65] Stuart Moss,et al. Current Status and Future Directions , 2013 .
[66] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[67] J. Carr,et al. The revolution in risk assessment and disease detection made possible with non-invasive imaging: implications for population science. , 2012, Ethnicity & disease.
[68] Jimmy D Bell,et al. Excess body fat in obese and normal-weight subjects , 2012, Nutrition Research Reviews.
[69] E. Vittinghoff,et al. Association of major and minor ECG abnormalities with coronary heart disease events. , 2012, JAMA.
[70] Jimmy D Bell,et al. The Missing Risk: MRI and MRS Phenotyping of Abdominal Adiposity and Ectopic Fat , 2012, Obesity.
[71] M. Szklo,et al. The Multi-Ethnic Study of Atherosclerosis (MESA) , 2012 .
[72] A. Dale,et al. Mild cognitive impairment: baseline and longitudinal structural MR imaging measures improve predictive prognosis. , 2011, Radiology.
[73] Anne Corbett,et al. Depression and dementia. , 2011, Mental health today.
[74] W. Brown. Framingham Heart Study. , 2011, Journal of clinical lipidology.
[75] H. Markus,et al. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis , 2010, BMJ : British Medical Journal.
[76] Abdominal fat from spine dual-energy x-ray absorptiometry and risk for subsequent diabetes. , 2010, The Journal of clinical endocrinology and metabolism.
[77] C. Jack,et al. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade , 2010, The Lancet Neurology.
[78] Udo Hoffmann,et al. Association of pericardial fat, intrathoracic fat, and visceral abdominal fat with cardiovascular disease burden: the Framingham Heart Study. , 2008, European heart journal.
[79] C. Roux,et al. DXA scanning in clinical practice. , 2008, QJM : monthly journal of the Association of Physicians.
[80] Christopher B. Kendall,et al. Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force. Endorsed by the Society for Vascular Medicine. , 2008, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.
[81] Philip Greenland,et al. Major and minor ECG abnormalities in asymptomatic women and risk of cardiovascular events and mortality. , 2007, JAMA.
[82] S. Kahn,et al. Review: The role of insulin resistance in nonalcoholic fatty liver disease. , 2006, The Journal of clinical endocrinology and metabolism.
[83] R. Ross,et al. Visceral Fat Is an Independent Predictor of All‐cause Mortality in Men , 2006, Obesity.
[84] Karl Swedberg,et al. Influence of Ejection Fraction on Cardiovascular Outcomes in a Broad Spectrum of Heart Failure Patients , 2005, Circulation.
[85] Ronald M Peshock,et al. The Dallas Heart Study: a population-based probability sample for the multidisciplinary study of ethnic differences in cardiovascular health. , 2004, The American journal of cardiology.
[86] R. Kronmal,et al. Multi-Ethnic Study of Atherosclerosis: objectives and design. , 2002, American journal of epidemiology.
[87] Francesco Fera,et al. The Amygdala Response to Emotional Stimuli: A Comparison of Faces and Scenes , 2002, NeuroImage.
[88] R. Collins,et al. Underestimation of risk associations due to regression dilution in long-term follow-up of prospective studies. , 1999, American journal of epidemiology.
[89] D. Hans,et al. How Hip and Whole-Body Bone Mineral Density Predict Hip Fracture in Elderly Women: The EPIDOS Prospective Study , 1998, Osteoporosis International.
[90] A. Folsom,et al. Correlates of uric acid and its association with asymptomatic carotid atherosclerosis: the ARIC Study. Atherosclerosis Risk in Communities. , 1996, Annals of epidemiology.
[91] R B D'Agostino,et al. Left atrial size and the risk of stroke and death. The Framingham Heart Study. , 1995, Circulation.
[92] Robert Epstein,et al. Comparison of methods for defining prevalent vertebral deformities: The study of osteoporotic fractures , 1995, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.
[93] Claus Christiansen,et al. Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. Report of a WHO Study Group. , 1994, World Health Organization technical report series.
[94] D. Levy,et al. Prognostic implications of echocardiographically determined left ventricular mass in the Framingham Heart Study. , 1990, The New England journal of medicine.
[95] M. Goklany. Report of Study Group I , 1973 .