Data sharing


Scientific Journals

Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook SA, Marvao Ad, Dawes T, O‘Regan DP, Kainz B, Glocker B and Rueckert D. Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation. IEEE Transactions on Medical Imaging. 2018;37:384-395.

Using 3D shape models to improve accuracy of heart segmentation and classify diseases.

Whiffin N, Walsh R, Govind R, Edwards M, Ahmad M, Zhang X, Tayal U, Buchan R, Midwinter W, Wilk AE, Najgebauer H, Francis C, Wilkinson S, Monk T, Brett L, O’Regan DP, Prasad SK, Morris-Rosendahl DJ, Barton PJR, Edwards E, Ware JS and Cook SA. CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation. Genet Med. 2018.

Tool for accurate interpretation of genetic variants in inherited cardiomyopathy.

Biffi C, de Marvao A, Attard M, Dawes TJW, Whiffin N, Bai W, Shi W, Francis C, Meyer H, Buchan R, Cook SA, Rueckert D, O’Regan DP. Three-Dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework. Bioinformatics. 2018; 34: 97-103 

A statistical approach for discovering genetic effects on the three-dimensional structure of the heart.

Suzuki H, Gao H, Bai W, Evangelou E, Glocker B, O’Regan DP, Elliott P and Matthews PM. Abnormal brain white matter microstructure is associated with both pre-hypertension and hypertension. PLoS ONE. 2017;12:e0187600.

Effect of early hypertension on brain structure in UK Biobank.

Cai J, Bryant JA, Le TT, Su B, de Marvao A, O’Regan DP, Cook SA and Chin CW. Fractal analysis of left ventricular trabeculations is associated with impaired myocardial deformation in healthy Chinese. J Cardiovasc Magn Reson. 2017;19:102.

Understanding relationship between trabecular complexity and contractile function. 

Dawes TJW, de Marvao A, Shi W, Fletcher T, Watson GMJ, Wharton J, Rhodes CJ, Howard LSGE, Gibbs JSR, Cook SA, Wilkins MR, O’Regan DP. Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study. Radiology. 2017;283 (2): 381-390                                                     

Using machine learning to predict patient outcomes in pulmonary hypertension from three dimensional models of the heart.

Schafer S, de Marvao A, Adami E, Fiedler LR, Ng B, Khin E, et al. Titin-truncating variants affect heart function in disease cohorts and the general population. Nature genetics. 2016;49(1):46-53

Defining the effects of titin mutations in health and disease – showing the mechanisms that prime the heart to fail in carriers.

Corden B, de Marvao A, Dawes TJ, Shi W, Rueckert D, Cook SA, O’Regan DP. Relationship between body composition and left ventricular geometry using three dimensional cardiovascular magnetic resonance. Journal of Cardiovascular Magnetic Resonance. 2016;18(1):32

Demonstrating how the geometry of the heart is adversely affected by changes in body composition.

Dawes TJ, Corden B, Cotter S, de Marvao A, Walsh R, Ware JS, Cook SA, O’Regan DP. Moderate Physical Activity in Healthy Adults Is Associated With Cardiac Remodeling. Circulation: Cardiovascular Imaging. 2016;9(8). pii: e004712

Even moderate exercise causes the heart to adapt and this data shows how knowledge of this is important to avoid over-diagnosis of cardiomyopathy in active adults.

Dawes TJ, Gandhi A, de Marvao A, Buzaco R, Tokarczuk P, Quinlan M, Durighel G, Diamond T, Monje Garcia L, de Cesare A, Cook SA, O’Regan DP. Pulmonary Artery Stiffness Is Independently Associated with Right Ventricular Mass and Function: A Cardiac MR Imaging Study. Radiology. 2016;280(2):398-404

The aorta becomes stiff as we age, but this paper shows how age-related pulmonary artery stiffness is also associated with changes in the structure and function of the right ventricle.

Bai W, Shi W, de Marvao A, Dawes TJ, O’Regan DP, Cook SA, Rueckert D. 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. 2016;26(1):133-145

How we created a detailed three dimensional model of the heart from high-resolution cardiac MRI data.

Roberts AM, Ware JS, Herman DS, Schafer S, Baksi J, Bick AG, Buchan RJ, Walsh R, John S, Wilkinson S, Mazzarotto F, Felkin LE, Gong S, MacArthur JA, Cunningham F, Flannick J, Gabriel SB, Altshuler DM, Macdonald PS, Heinig M, Keogh AM, Hayward CS, Banner NR, Pennell DJ, O’Regan DP, San TR, de Marvao A, Dawes TJ, Gulati A, Birks EJ, Yacoub MH, Radke M, Gotthardt M, Wilson JG, O’Donnell CJ, Prasad SK, Barton PJ, Fatkin D, Hubner N, Seidman JG, Seidman CE, Cook SA. Integrated allelic, transcriptional, and phenomic dissection of the cardiac effects of titin truncations in health and disease. Sci Transl Med. 2015;7:270ra276

Large scale study of titin variation after its initial identification as an important cause of cardiomyopathy.  Identified methods for interpreting genetic variation in health and disease and opportunities for stratified medicine that may be generically applied across Mendelian diseases.

de Marvao A, Dawes TJ, Shi W, Durighel G, Rueckert D, Cook SA, O’Regan DP. Precursors of Hypertensive Heart Phenotype Develop in Healthy Adults: A High-Resolution 3D MRI Study. JACC Cardiovasc Imaging. 2015;8:1260-1269

Using 3D cardiac models to understand the earliest effects of hypertension on the heart.

Buyandelger B, Mansfield C, Kostin S, Choi O, Roberts AM, Ware JS, Mazzarotto F, Pesce F, Buchan R, Isaacson RL, Vouffo J, Gunkel S, Knoll G, McSweeney SJ, Wei H, Perrot A, Pfeiffer C, Toliat MR, Ilieva K, Krysztofinska E, Lopez-Olaneta MM, Gomez-Salinero JM, Schmidt A, Ng KE, Teucher N, Chen J, Teichmann M, Eilers M, Haverkamp W, Regitz-Zagrosek V, Hasenfuss G, Braun T, Pennell DJ, Gould I, Barton PJ, Lara-Pezzi E, Schafer S, Hubner N, Felkin LE, O’Regan DP, Brand T, Milting H, Nurnberg P, Schneider MD, Prasad S, Petretto E, Knoll R. ZBTB17 (MIZ1) Is Important for the Cardiac Stress Response and a Novel Candidate Gene for Cardiomyopathy and Heart Failure. Circ Cardiovasc Genet. 2015;8:643-652

Shi W, Lombaert H, Bai W, Ledig C, Zhuang X, Marvao A, Dawes T, O’Regan D, O’Regan D. Multi-atlas spectral PatchMatch: application to cardiac image segmentation. Med Image Comput Comput Assist Interv. 2014;17:348-355

de Marvao A, Dawes TJ, Shi W, Minas C, Keenan NG, Diamond T, Durighel G, Montana G, Rueckert D, Cook SA, O’Regan DP. Population-based studies of myocardial hypertrophy: high resolution cardiovascular magnetic resonance atlases improve statistical power. J Cardiovasc Magn Reson. 2014;16:16

Woodbridge M, Fagiolo G, O’Regan DP.  MRIdb: medical image management for biobank research. J Digit Imaging. 2013;26:886-890

Shi W, Caballero J, Ledig C, Zhuang X, Bai W, Bhatia K, de Marvao AM, Dawes T, O’Regan D, Rueckert D. Cardiac image super-resolution with global correspondence using multi-atlas patchmatch. Med Image Comput Comput Assist Interv. 2013;16:9-16

Corden B, Keenan NG, de Marvao AS, Dawes TJ, Decesare A, Diamond T, Durighel G, Hughes AD, Cook SA, O’Regan DP. Body fat is associated with reduced aortic stiffness until middle age. Hypertension. 2013;61:1322-1327

Bai W, Shi W, O’Regan DP, Tong T, Wang H, Jamil-Copley S, Peters NS, Rueckert D. A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac MR images. IEEE Trans Med Imaging. 2013;32:1302-1315