SmartHeart publications

Journal Papers

Ricci F., Aung N., Gallina S., Zemrak F., Fung K., Bisaccia G., Paiva J.M., Khanji M.Y., Mantini C., Palermi S., Lee A.M., Piechnik S.K., Neubauer S., Petersen S.E., Schelbert E., Manisty C. and Moon J.C. (2021) Cardiovascular magnetic resonance reference values of mitral and tricuspid annular dimensions: the UK Biobank cohort. Journal of Cardiovascular Magnetic Resonance, 23(1):1-13. https://doi.org/10.1186/s12968-020-00688-y

Raisi-Estabragh Z., Izquierdo C., Campello V.M., Martin-Isla C., Jaggi A., Harvey N., Lekadir K. and Petersen S.E. (2020) Cardiac magnetic resonance radiomics: basic principles and clinical perspectives. European Heart Journal-Cardiovascular Imaging, 21 (4), 349–356. https://doi.org/10.1093/ehjci/jeaa028

Aung N., Khanji M.Y., Munroe P.B. and Petersen S.E. (2020) Causal Inference for Genetic Obesity, Cardiometabolic Profile and COVID-19 Susceptibility: A Mendelian Randomization Study. Frontiers in genetics, 11:586308. doi: 10.3389/fgene.2020.586308.

Ng M., Guo F., Biswas L., Petersen S.E., Piechnik S.K., Neubauer S. and Wright G. (2020), Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study, arXiv preprint, arXiv:2012.15772, 2020.

Zheng Q., Delingette H., Fung K., Petersen S.E. and Ayache N. (2020) Pathological Cluster Identification by Unsupervised Analysis in 3,822 UK Biobank Cardiac MRIs. Frontiers in Cardiovascular Medicine, 7:539788. doi: 10.3389/fcvm.2020.539788

Raisi‐Estabragh Z., Biasiolli L., Cooper J., Aung N., Fung K., Paiva J.M., Sanghvi M.M., Thomson R.J., Curtis E., Paccou J., Rayner J.J., Werys K., Puchta H., Thomas K.E., Lee A.M., Piechnik S.K., Neubauer S., Munroe P.B., Cooper C., Petersen S.E. and Harvey N.C. (2020) Poor bone quality is associated with greater arterial stiffness: insights from the UK Biobank. Journal of Bone and Mineral Research, 36: 90-99. https://doi.org/10.1002/jbmr.4164

Cetin I., Raisi-Estabragh Z., Petersen S.E., Napel S., Piechnik S.K., Neubauer S., Ballester M.A.G., Camara O. and Lekadir K. (2020) Radiomics signatures of cardiovascular risk factors in cardiac MRI: Results from the UK Biobank. Frontiers in cardiovascular medicine, 7:591368. https://doi.org/10.3389/fcvm.2020.591368

Raisi-Estabragh Z., Gkontra P., Jaggi A., Cooper J., Augusto J., Bhuva A.N., Davies R.H., Manisty C.H., Moon J.C., Munroe P.B., Harvey N.C., Lekadir K. and Petersen S.E. (2020) Repeatability of Cardiac Magnetic Resonance Radiomics: A Multi-Centre Multi-Vendor Test-Retest Study. Frontiers in cardiovascular medicine, 7:586236. https://doi.org/10.3389/fcvm.2020.58623

Aung N., Sanghvi M.M., Piechnik S.K., Neubauer S., Munroe P.B. and Petersen S.E. (2020) The effect of blood lipids on the left ventricle: a Mendelian randomization study. Journal of the American College of Cardiology, 76(21):2477-2488, https://doi.org/10.1016/j.jacc.2020.09.583

Cruz G., Jaubert O., Qi H., Bustin A., Milotta G., Schneider T., Koken P., Doneva M., Botnar R.M. and Prieto C. (2020) 3D free-breathing cardiac magnetic resonance fingerprinting. NMR Biomed. 2020 Oct;33(10):e4370. doi: 10.1002/nbm.4370. Epub 2020 Jul 21. PMID: 32696590.

Puyol-Antón E., Ruijsink B., Baumgartner C.F., Masci P.-G., Sinclair M., Konukoglu E., Razavi R. and King A.P. (2020) Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with uncertainty-based quality-control. Journal of Cardiovascular Magnetic Resonance 22, 60 (2020). https://doi.org/10.1186/s12968-020-00650-y

Oksuz I., Clough J., Ruijsink B., Puyol Anton E., Bustin A., Lima da Cruz G., Prieto Vasquez C., King A. and Schnabel J. (2020) Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation. IEEE Transactions on Medical Imaging,vol. 39, no. 12, pp. 4001-4010, Dec. 2020, doi: 10.1109/TMI.2020.3008930.

Clough J., Byrne N., Oksuz I., Zimmer V., Schnabel J. and King A. (2020). A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology. IEEE Transactions on Pattern Analysis and Machine Intelligence. DOI: 10.1109/TPAMI.2020.3013679.

Bai W., Suzuki H., Huang J., Francis C., Wang S., Tarroni G., Guitton F., Aung N., Fung K., Petersen S.E., Piechnik S.K., Neubauer S., Evangelou E., Dehghan A., O’Regan D.P., Wilkins M.R., Guo Y., Matthews P.M. and Rueckert D. (2020). A population-based phenome-wide association study of cardiac and aortic structure and function, Nature Medicine 26(10):1654-1662. DOI: 10.1038/s41591-020-1009-y. Epub 2020 Aug 24.PMID: 32839619.

Nordio G., Schneider T., Cruz G., Correia T., Bustin A., Prieto C., Botnar R.M. and Henningsson M. (2020). Whole-heart T1 mapping using a 2D fat image navigator for respiratory motion compensation. Magnetic Resonance in Medicine. 83(1):178-187. DOI: 10.1002/mrm.27919. PMID: 31400054 PMCID: PMC6791811

Rueckert D. and Schnabel, J.A. (2020). Model-Based and Data-Driven Strategies in Medical Image Computing, Proceedings of the IEEE, 108(1):110-124. DOI: 10.1109/JPROC.2019.2943836.

Duchateau N., King A. and De Craene M. (2020) Machine Learning Approaches for Myocardial Motion and Deformation Analysis, Frontiers in Cardiovascular Medicine, 09 January 2020. DOI: 10.3389/fcvm.2019.00190

Martin-Isla C., Campello V.M., Izquierdo C., Raisi-Estabragh Z., Baeßler B., Petersen S.E. and Lekadir K. (2020). Image-Based Cardiac Diagnosis With Machine Learning: A Review. Frontiers in Cardiovascular Medicine, 7:1. DOI: 10.3389/fcvm.2020.00001.

Zhang L., Gooya A., Pereanez M., Dong B., Piechnik S., Neubauer S., Petersen S. and Frangi A.F. (2019). Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN. IEEE Transactions on Biomedical Engineering, 66(7):1975-1986. DOI: 10.1109/tbme.2018.2881952 PMID: 30475705

Alansary A., Oktay O., Li Y., Folgoc L.L., Hou B., Vaillant G., Kamnitsas K., Vlontzos A., Glocker B., Kainz B. and Rueckert D. (2019). Evaluating reinforcement learning agents for anatomical landmark detection. Medical Image Analysis, 53:156-164. DOI: 10.1016/j.media.2019.02.007. PMID: 30784956

Meng Q., Housden J., Matthew J., Rueckert D., Schnabel J.A., Kainz B., Sinclair M., Zimmer V., Hou B., Rajchl M., Toussaint N., Oktay O., Schlemper J. and Gomez A. (2019). Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging. IEEE Transactions on Medical Imaging. 38(12):2755-2767. DOI: 10.1109/TMI.2019.2913311. PMID: 31021795 PMCID: PMC6892638.

Lavin Plaza B., Theodoulou I., Rashid I., Hajhosseiny R., Phinikaridou A. and Botnar R.M. (2019). Molecular Imaging in Ischemic Heart Disease. Current Cardiovascular Imaging Reports. 12(7):31. DOI: 10.1007/s12410-019-9500-x. PMID: 31281564 PMCID: PMC6557873.

Schlemper J., Oktay O., Schaap M., Heinrich M., Kainz B., Glocker B., and Rueckert D. (2019). Attention gated networks: Learning to leverage salient regions in medical images. Medical Image Analysis, 53:197-207. DOI: 10.1016/j.media.2019.01.012.

Zheng Q., Delingettea H., Fung K., Petersen S.E. and Ayache N. (2019) Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank. Computer Vision and Pattern Recognition. arXiv:1902.05811 [cs.CV]

Ricci F., Aung N., Thomson R., Boubertakh R., Camaioni C., Doimo S., Sanghvi M.M., Fung K., Khanji M.Y., Lee A., Malcolmson J., Mantini C., Paiva J., Gallina S., Fedorowski A., Mohiddin S.A., Aquaro G.D. and Petersen S.E. (2019). Pulmonary blood volume index as a quantitative biomarker of haemodynamic congestion in hypertrophic cardiomyopathy, European Heart Journal-Cardiovascular Imaging, 20 (12):1368-1376. DOI: 10.1093/ehjci/jez213.

Petersen S.E., Abdulkareem M. and Leiner T. (2019). Artificial intelligence will transform cardiac imaging–opportunities and challenges, Frontiers in cardiovascular medicine, 6:133. DOI: 10.3389/fcvm.2019.00133.

Mauger C., Gilbert K., Lee A.M., Sanghvi M.M., Aung N., Fung K., Carapella V., Piechnik S.K., Neubauer S., Petersen S.E., Suinesiaputra A. and Young A.A. (2019). Right ventricular shape and function: cardiovascular magnetic resonance reference morphology and biventricular risk factor morphometrics in UK Biobank, Journal of Cardiovascular Magnetic Resonance, 21 (1): 41. DOI: 10.1186/s12968-019-0551-6.

Khanji M.Y., Jensen M.T., Kenawy A.A., Raisi-Estabragh Z., Paiva J.M., Aung N., Fung K., Lukaschuk E., Zemrak F., Lee A.M., Barutcu A., Maclean E., Cooper J., Piechnik S.K., Neubauer S. and Petersen S.E. (2019). Association Between Recreational Cannabis Use and Cardiac Structure and Function. JACC: Cardiovascular imaging, 13(3):886-888. DOI: 10.1016/j.jcmg.2019.10.012

Jensen M.T., Fung K., Aung N., Sanghvi M.M., Chadalavada S., Paiva J.M., Khanji M.Y., de Knegt M.C., Lukaschuk E., Lee A.M., Barutcu A., Maclean E., Carapella V., Cooper J., Young A., Piechnik S.K., Neubauer S. and Petersen S.E. (2019). Changes in cardiac morphology and function in individuals with diabetes mellitus: the UK Biobank cardiovascular magnetic resonance substudy. Circulation: Cardiovascular Imaging, 12(9):e009476. DOI: 10.1161/CIRCIMAGING.119.009476

Guo F., Ng M., Goubran M., Petersen S.E., Piechnik S.K., Neubauer S. and Wright G. (2020). Improving Cardiac MRI Convolutional Neural Network Segmentation on Small Training Datasets and Dataset Shift: A Continuous Kernel Cut Approach, Medical Image Analysis, 61:101636. DOI: 10.1016/j.media.2020.101636

Fung K., Ramírez J., Warren H.R., Aung N., Lee A.M., Tzanis E., Petersen S.E. and Munroe P.B. (2019). Genome-wide association study identifies loci for arterial stiffness index in 127,121 UK Biobank participants, Scientific Reports, 9(1):9143. DOI: 10.1038/s41598-019-45703-0

Elmahi E., Sanghvi M.M., Jones A., Aye C.Y., Lewandowski A.J., Aung N., Cooper J.A., Paiva J.M., Lukaschuk E., Piechnik S.K., Neubauer S., Petersen S.E. and Leeson P. (2019). Does self-reported pregnancy loss identify women at risk of an adverse cardiovascular phenotype in later life? Insights from UK Biobank, PloS one, 14 (10). DOI: 10.1371/journal.pone.0223125

Chen C., Bai W., Davies R.H., Bhuva A.N., Manisty C., Moon J.C., Aung N., Lee A.M., Sanghvi M.M., Fung K., Paiva J.M. (2019). Improving the generalizability of convolutional neural network-based segmentation on CMR images. Front. Cardiovasc. Med. 7:105. doi: 10.3389/fcvm.2020.00105.

Bhuva A.N., Bai W., Lau C., Davies R.H., Ye Y., Bulluck H., McAlindon E., Culotta V., Swoboda P.P., Captur G., Treibel T.A., Augusto J.B., Knott K.D., Seraphim A., Cole G.D., Petersen S.E., Edwards N.C., Greenwood J.P., Bucciarelli-Ducci C., Hughes A.D., Rueckert D., Moon J.C. and Manisty C.H. (2019). A Multicenter, Scan-Rescan, Human and Machine Learning CMR Study to Test Generalizability and Precision in Imaging Biomarker Analysis. Circulation: Cardiovascular Imaging, 12 (10):e009214. DOI: 10.1161/CIRCIMAGING.119.009214

Beyer S.E., Petersen S.E. (2019) Advances in population-based imaging using cardiac magnetic resonance. Progress in Biomedical Engineering, 1 (1):012003. DOI: 10.1088/2516-1091/ab3369

Aung N., Vargas J.D., Yang C., Cabrera C.P., Warren H.R., Fung K., Tzanis E., Barnes M.R., Rotter J.I., Taylor K.D., Manichaikul A.W., Lima J.A.C., Bluemke D.A., Piechnik S.K., Neubauer S., Munroe P.B., Petersen S.E. (2019). Genome-wide analysis of left ventricular image-derived phenotypes identifies fourteen loci associated with cardiac morphogenesis and heart failure development. Circulation, 140 (16): 1318-1330. DOI: 10.1161/CIRCULATIONAHA.119.041161

Attar R., Pereañez M., Gooya A., Albà X., Zhang L., de Vila M.H., Lee A.M., Aung N., Lukaschuk E., Sanghvi M.M. and Fung K. (2019). Quantitative CMR population imaging on 20,000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation. Medical image analysis, 56:26-42, S1361-8415(19)30048-9. DOI: 10.1016/j.media.2019.05.006

Duan J., Bello G., Schlemper J., Bai W., Dawes T. J., Biffi C., De Marvao A., Doumou G., O’Regan D. P. and Rueckert D. (2019). Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach. IEEE transactions on medical imaging, 38:9, 2151-2164. DOI: 10.1109/TMI.2019.2894322.

Qi H., Bustin A., Cruz G., Jaubert O., Chen H., Botnar R.M., Prieto C. (2019) Free-running simultaneous myocardial T1/T2 mapping and cine imaging with 3D whole-heart coverage and isotropic spatial resolution. Magn Reson Imaging. 63:159-169. PMID: 31425810 DOI: 10.1016/j.mri.2019.08.008.

Jaubert O., Cruz G., Bustin A., Schneider T., Lavin B., Koken P., Hajhosseiny R., Doneva M., Rueckert D., Botnar R. M. and Prieto C. (2019) Water–fat Dixon cardiac magnetic resonance fingerprinting. Magn Reson Med. 2019; 00: 1– 17. https://doi.org/10.1002/mrm.28070

Milotta G., Ginami G., Bustin A., Neji R., Prieto C. and Botnar R. M. (2019). 3D Whole-heart free-breathing qBOOST-T2 mapping. Magn. Reson. Med., available online 21 October 2019. PMID: 31631378 DOI: 10.1002/mrm.28039

Ruijsink B., Puyol-Antón E., Oksuz I., Sinclair M., Bai W., Schnabel J.A., Razavi R. and King A.P. (2019) Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function. JACC: Cardiovascular Imaging, 13(3): 696-698. PMID: 31326477. https://doi.org/10.1016/j.jcmg.2019.05.030

Küstner T., Bustin A., Jaubert O., Neji R., Prieto C. and Botnar R. (2019) 3D Cartesian fast interrupted steady‐state (FISS) imaging. Magn. Reson. Med. 82(5):1617-1630. PMID: 31197881. doi: 10.1002/mrm.27830

Cruz G., Jaubert O., Botnar R. M. and Prieto C. (2019) Cardiac Magnetic Resonance Fingerprinting: Technical Developments and Initial Clinical Validation. Current Cardiology Reports 21(9):91. PMCID: PMC6661029. PMID: 31352620. doi: 10.1007/s11886-019-1181-1.

Qi, H., Jaubert, O., Bustin, A., Cruz, G., Chen, H., Botnar, R. and Prieto, C. (2019) Free-running 3D whole heart myocardial T1 mapping with isotropic spatial resolution. Magn Reson Med 82: 1331-1342. PMID: 31099442  doi: 10.1002/mrm.27811.

Munoz, C., Cruz, G., Neji, R., Botnar, R.M. & Prieto, C. (2019) Motion corrected water/fat whole-heart coronary MR angiography with 100% respiratory efficiency. Magn Reson Med 82: 732-742. PMID: 30927310 PMCID: PMC6563440  doi: 10.1002/mrm.27732.

Oksuz, I., Ruijsink, J. B., Puyol Anton, E., Clough, J. R., Lima da Cruz, G. J., Bustin, A., Prieto Vasquez, C., Botnar, R. M., Rueckert, D., Schnabel, J. A. and King, A. P. (2019) Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning. Medical Image Analysis, 55: 136-147. DOI: 10.1016/j.media.2019.04.009

Milotta G., Ginami G., Cruz G., Neji R., Prieto C. and Botnar R.M. (2019) Simultaneous 3D whole-heart bright-blood and black blood imaging for cardiovascular anatomy and wall assessment with interleaved T2 prep-IR. agn Reson Med. 2019 Jul;82(1):312-325. doi: 10.1002/mrm.27734. Epub 2019 Mar 21. PMID: 30896049

Bustin A., Cruz G., Jaubert O., Lopez K., Botnar R. M. and Prieto C. (2019). High-dimensionality undersampled patch-based reconstruction (HD-PROST) for accelerated multi-contrast MRI. Magn Reson Med. 2019 Jun;81(6):3705-3719. doi: 10.1002/mrm.27694. Epub 2019 Mar 4. PMID: 30834594. ttps://doi.org/10.1002/mrm.27694

Robinson, R., Valindria V. V., Bai W., Oktay, O., Kainz, B., Suzuki H. et al. (2019) Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study. Journal of Cardiovascular Magnetic Resonance 21:18. https://doi.org/10.1186/s12968-019-0523-x

Woodbridge S.P., Aung N., Paiva J.M., et al. (2019) Physical activity and left ventricular trabeculation in the UK Biobank community-based cohort study.  Heart Published Online First: 05 February 2019. doi: 10.1136/heartjnl-2018-314155

Gilbert K., Bai W., Mauger C., Medrano-Gracia P., Suinesiaputra A., Lee A. M., Sanghvi M. M., Aung N., Piechnik S. K., Neubauer S., Petersen S. E., Rueckert D. and Young A. A. (2019). Independent Left Ventricular Morphometric Atlases Show Consistent Relationships with Cardiovascular Risk Factors: A UK Biobank Study. Scientific Reports, 9:1130. PMID: 30718635 PMCID: PMC6362245. https://doi.org/10.1038/s41598-018-37916-6

Beyer S.E., Sanghvi M.M., Aung N., Hosking A., Cooper J.A., Paiva J.M., et al. (2018) Prospective association between handgrip strength and cardiac structure and function in UK adults. PLoS ONE 13(3): e0193124. https://doi.org/10.1371/journal.pone.0193124

Cruz G., Bustin A., Jaubert O., Schneider T., Botnar R. M. and Prieto C. (2019). Sparsity and locally low rank regularization for magnetic resonance fingerprinting. Magn Reson Med. 2019;1–14. DOI: https://doi.org/10.1002/mrm.27665

Sanghvi M.M., Aung N., Cooper J.A., Paiva J.M., Lee A.M. et al. (2018) The impact of menopausal hormone therapy (MHT) on cardiac structure and function: Insights from the UK Biobank imaging enhancement study. PLoS One. 2018 Mar 8;13(3):e0194015. PMID: 29518141 PMCID: PMC5843282  doi: 10.1371/journal.pone.0194015. eCollection 2018.

Thomson R. J., Aung N., Sanghvi M. M., Paiva J. M., Lee A. M., Zemrak F., Fung K…. and Petersen, S. E. (2018) Variation in lung function and alterations in cardiac structure and function – Analysis of the UK Biobank cardiovascular magnetic resonance imaging substudy. PLoS ONE 13(3): e0194434. PMID:29558496 PMCID:PMC5860758. https://doi.org/10.1371/journal.pone.0194434

Qin C., Hajnal J. V., Rueckert D., Schlemper J., Caballero J., Price A. N…. and Rueckert D. (2018). Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction. IEEE transactions on medical imaging, 38(1):280-290. PMID:30080145. DOI:  10.1109/TMI.2018.2863670

Tarroni G., Oktay O., Bai W., Schuh A., Suzuki H., Passerat-Palmbach J., de Marvao A…. Rueckert D. (2018). Learning-Based Quality Control for Cardiac MR Images. IEEE transactions on medical imaging. arXiv:1803.09354v2.  PMID:30403623 DOI: 10.1109/TMI.2018.2878509

Aung N., Sanghvi M., Zemrak F., Lee A., Cooper J., Paiva J….. Petersen S., (2018). Association Between Ambient Air Pollution and Cardiac Morpho-Functional Phenotypes: Insights From the UK Biobank Population Imaging Study. Circulation, 138 (20), pp. 2175-2186. PMID:30524134 PMCID:PMC6250297. DOI: 10.1161/CIRCULATIONAHA.118.034856

Puyol-Antón E., Ruijsink B., Gerber B., Amzulescu M. S., Langet H., De Craene M., Schnabel J. A., Piro P. and King A. P. (2018). Regional Multi-view Learning for Cardiac Motion Analysis: Application to Identification of Dilated Cardiomyopathy Patients. IEEE Trans Biomed Eng. 2018 Aug 15. PMID: 30113891 DOI: 10.1109/TBME.2018.2865669.

Cruz G., Schneider T., Bruijnen T., Gaspar A. S., Botnar R. M. and Prieto C. (2018). Accelerated magnetic resonance fingerprinting using soft-weighted key-hole (MRF-SOHO). PloS one. 2018 Aug 9;13(8):e0201808. PMID: 30092033 PMCID: PMC6084944      DOI: 10.1371/journal.pone.0201808

Cruz G., Jaubert O., Schneider T., Botnar R.M. and Prieto C. (2018). Rigid motion corrected magnetic resonance fingerprinting. Magnetic Resonance in Medicine, Magn Reson Med. 2019 Feb;81(2):947-961. doi: 10.1002/mrm.27448. Epub 2018 Sep 3. PMID: 30229558. DOI: 10.1002/mrm.27448 

Bustin A., Ginami G., Cruz G., Correia T., Ismail TF., Rashid R., Neji R., Botnar RM., Prieto C. (2018) Five-minute whole-heart coronary MRA with sub-millimeter isotropic resolution, 100% respiratory scan efficiency and 3D-PROST reconstruction. Magnetic Resonance in Medicine, 81(1):102-115. DOI: https://doi.org/10.1002/mrm.27354.

Bodagh N., Archbold R.A., Weerackody R., Hawking M.K.D., Barnes M.R., Lee A.M., Janjuha S., Gutteridge C., Robson J. and Timmis A. (2018) Feasibility of real-time capture of routine clinical data in the electronic health record: a hospital-based, observational service-evaluation study. BMJ Open 2018;8:e019790. DOI:10.1136/bmjopen-2017-019790

Abbott T.E.F., Gooneratne M., McNeill J., Lee A., Levett D.Z.H., Grocott M.P.W., Swart M., MacDonald N.; ARCTIC study investigators (2018). Inter-observer reliability of preoperative cardiopulmonary exercise test interpretation: a cross-sectional study. British Journal of Anaesthesia, 120(3), 475-483. DOI: 10.1016/j.bja.2017.11.071

Suinesiaputra, A., Sanghvi, M. M., Aung, N., Paiva, J. M., Zemrak, F., Fung, K., Lukaschuk, E., Lee, A.M., Carapella, V., Kim. Y. J., Francis, J., Piechnik, S.K., Neubauer, S., Greiser, A., Jolly, M-P., Hayes, C., Young, A.A. and Petersen, S. E. (2018). Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results. The International Journal of Cardiovascular Imaging, 34(2), 281–291.  DOI: 10.1007/s10554-017-1225-9. PMCID: PMC5809564, PMID: 28836039

Bai W., Sinclair M., Tarroni G., Oktay O., Rajchl M., Vaillant G., Lee A.M., Aung N., Lukaschuk E., Sanghvi M.M., Zemrak F., Fung K., Paiva J.M., Carapella V., Kim Y.J., Suzuki H., Kainz B., Matthews P.M., Petersen S.E., Piechnik S.K., Neubauer S., Glocker B., and Rueckert D. (2018) Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. Journal of Cardiovascular Magnetic Resonance, accepted. eprint arXiv:1710.09289

Correia T., Ginami G., Cruz G., Neji R., Rashid I., Botnar RM., Prieto C. (2018) Optimized respiratory-resolved motion-compensated 3D Cartesian coronary MR angiography. Magnetic Resonance in Medicine, 2018:1-12. doi: 10.1002/mrm.27208.

Abbott T.E.F., Minto G., Lee A.M., Pearse R.M. and Ackland G.L. (2017). Elevated preoperative heart rate is associated with cardiopulmonary and autonomic impairment in high-risk surgical patients. British Journal of Anaesthesia, 119(1) 87-94. DOI: 10.1093/bja/aex164

Nordio G., Henningsson M., Chiribiri A., Villa A.D.M., Schneider T. and Botnar R.M. (2017). 3D mycoardial T1 mapping using saturation recovery. Journal of Magnetic Resonance in Medicine, 46(1):218-227. DOI: 10.1002/jmri.25575. PMCID: PMC5518207

Holtackers, R. J., Chiribiri, A., Schneider, T., Higgins, D. M., & Botnar, R. M. (2017). Dark-blood late gadolinium enhancement without additional magnetization preparation. Journal of Cardiovascular Magnetic Resonance, 19, 64. DOI: 10.1186/s12968-017-0372-4. PMCID: PMC5568308. PMID: 28835250

Ginami G., Neji R., Phinikaridou A., Whitaker J., Botnar R. M. and Prieto C. (2018) Simultaneous bright- and black-blood whole-heart MRI for noncontrast enhanced coronary lumen and thrombus visualization. Magn. Reson. Med, 79: 1460–1472. DOI: 10.1002/mrm.26815. PMID: 28722267

Ginami G., Neji R., Rashid I., Chiribiri A., Ismail T. F., Botnar R. M. and Prieto C. (2018) 3D whole-heart phase sensitive inversion recovery CMR for simultaneous black-blood late gadolinium enhancement and bright-blood coronary CMR angiography. J Cardiovasc Magn Reson. 19: 94. DOI: 10.1186/s12968-017-0405-z. PMCID: PMC5702978

Henningsson M., Jouke S., van Ensbergen G. and Botnar R. (2018) Coronary MR Angiography Using Image-Based Respiratory Motion Compensation With Inline Correction and Fixed Gating Efficiency. Magnetic Resonance in Medicine 79:416–422. DOI: 10.1002/mrm.26678. PubMed PMID: 28321900. PMCID: 5763408

Schlemper J., Caballero J., Hajnal J. V., Price A. and Rueckert D. (2017) A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction in IEEE Transactions on Medical Imaging, vol. PP, no. 99, pp. 1-1.
doi: 10.1109/TMI.2017.2760978

Puyol-Antón E., Sinclair M., Gerber B., Silvia Amzulescu M., Langet H., De Craene M., Aljabar P., Piro P. and King A. P. (2017) A multimodal spatiotemporal cardiac motion atlas from MR and ultrasound data. Medical Image Analysis 40: 96–110. doi: 10.1016/j.media.2017.06.002. PubMed PMID: 28646674.

Oktay O., Ferrante E., Kamnitsas K., Heinrich M., Bai W., Caballero J., Guerrero R., Cook S., de Marvao A., Dawes T., O’Regan D., Kainz B., Glocker B. and Rueckert D. (2017) Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation. IEEE Transactions on Medical Imaging ( arXiv:1705.08302). doi: 10.1109/TMI.2017.2743464. PMID: 28961105.  https://arxiv.org/pdf/1705.08302.pdf

Conference Papers

Petersen S.E., Ballester M.A.G. and Lekadir K. (2020) Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI, Pop, Mihaela, et al., eds. Statistical Atlases and Computational Models of the Heart., 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Revised Selected Papers. Vol. 12009. Springer Nature, 2020. https://doi.org/10.1007/978-3-030-39074-7_31

Ruijsink B., Puyol-Anton E., Li Y., Bai W., Kerfoot E., Razavi R. and King A.P. (2020) Quality-aware semi-supervised learning for CMR segmentation. MICCAI STACOM 2020, https://arxiv.org/pdf/2009.00584.pdf

Ouyang C., Biffi C., Chen C., Kart T., Qiu H. and Rueckert D. (2020) Self-supervision with Superpixels: Training Few-Shot Medical Image Segmentation without Annotation. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12374. Springer, Cham. https://doi.org/10.1007/978-3-030-58526-6_45

Lu P., Qiu H., Qin C., Bai W., Rueckert D., Noble J.A. (2020) Going Deeper into Cardiac Motion Analysis to Model Fine Spatio-Temporal Features. In: Papież B., Namburete A., Yaqub M., Noble J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_23

Qin C., Wang S., Chen C., Qiu H., Bai W. and Rueckert D. (2020) Biomechanics-informed neural network for myocardial motion tracking in MRI. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2020. https://arxiv.org/pdf/2006.04725.pdf

Chen C., Qin C., Qiu H., Ouyang C., Wang S., Chen L., Tarroni G., Bai W. and Rueckert D. (2020) Realistic adversarial data augmentation for MR image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2020. https://arxiv.org/abs/2006.13322

Puyol-Anton E., Chen C., Clough J.R., Ruijsink B., Sidhu B.S., Gould J., Porter B., Elliott M., Mehta V., Rueckert D., Rinaldi C.A. and King A.P. (2020) Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12261. Springer, Cham. https://doi.org/10.1007/978-3-030-59710-8_28

Li L., Zimmer V.A., Ding W., Wu F., Schnabel J.A. and Zhuang X. (2020) Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information. MICCAI workshop: STACOM 2020, in press
https://arxiv.org/pdf/2008.12205.pdf

Li L., Weng X., Schnabel J.A. and Zhuang X. (2020). Joint left atrial segmentation and scar quantification based on a DNN with spatial encoding and shape attention. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12264. Springer, Cham. https://arxiv.org/pdf/2006.13011.pdf

Wang S., Tarroni G., Qin C., Mo Y., Dai C., Chen C., Glocker B., Guo Y., Rueckert D.and Bai W. (2020). Deep generative model-based quality control for cardiac MRI segmentation. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12264. Springer, Cham. https://doi.org/10.1007/978-3-030-59719-1_9

Qin C., Schlemper J., Duan J., Seegoolam G., Price A., Hajnal J. and Rueckert D. (2019) k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-Temporal Correlations. In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_56 

Seegoolam G., Schlemper J., Qin C., Price A., Hajnal J. and Rueckert D. (2019) Exploiting motion for deep learning reconstruction of extremely-undersampled dynamic MRI. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, pp.704-712. DOI: 10.1007/978-3-030-32251-9_77

Ouyang C., Kamnitsas K., Biffi C., Duan J. and Rueckert D. (2019) Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation. In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_74

Chen C., Ouyang C., Tarroni G., Schlemper J., Qiu H., Bai W. and Rueckert D. (2020) Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation. In: Pop M. et al. (eds) Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. STACOM 2019. Lecture Notes in Computer Science, vol 12009. Springer, Cham. https://doi.org/10.1007/978-3-030-39074-7_22

Chen C., Biffi C., Tarroni G., Petersen S., Bai W. and Rueckert D. (2019) Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images. In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention ‚ MICCAI 2019. Lecture Notes in Computer Science, vol 11765. Springer, Cham. DOI: 10.1007/978-3-030-32245-8_58.

Duan J., Schlemper J., Qin C., Ouyang C., Bai W., Biffi C., Bello G., Statton B., O’Regan D. and Rueckert D. (2019) VS-Net: Variable splitting network for accelerated parallel MRI reconstruction. In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11767. Springer, Cham. https://arxiv.org/pdf/1907.10033.pdf

Cruz G., Jaubert O., Schneider T., Freitas A., Henningsson M., Botnar R. M. and Prieto C. (2019) 3D Cardiac Magnetic Resonance Fingerprinting. In Proceedings SCMR 2019.

Cruz G., Jaubert O., Schneider T., Bustin A., Botnar R. M. and Prieto C. (2019) Toward 3D Free-breathing Cardiac Magnetic Resonance Fingerprinting. In Proceedings ISMRM 2019, 4385.

Jaubert O., Cruz G., Bustin A., Schneider T., Botnar R. M. and Prieto C. (2019) Dixon-cMRF: cardiac tissue characterization using three-point Dixon MR fingerprinting. In Proceedings ISMRM 2019, 1100.

Oksuz I., Clough J., Ruijsink B., Puyol-Antón E., Bustin A., Cruz G., Prieto C., Rueckert D., King A.P. and Schnabel J. A. (2019) Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space. In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11767. Springer, Cham. https://arxiv.org/abs/1906.05695.

Clough J.R., Oksuz I., Puyol-Antón E., Ruijsink B., King A.P. and Schnabel J. A. (2019) Global and Local Interpretability for Cardiac MRI Classification. In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_72

Bai W., Chen C., Tarroni G., Duan J., Guitton F., Petersen S. E., Guo Y., Matthews P. M. and Rueckert D. (2019) Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction. MICCAI, 2019, accepted. arXiv:1907.02757v1

Oksuz, İ., Cruz G., Clough J., Bustin A., Nicolo F., Botnar R.M., Prieto C., King A.P. and Schnabel J.A. (2019), Magnetic Resonance Fingerprinting using Recurrent Neural Networks, ISBI, 2019.  arXiv:1812.08155

Oksuz, I., Clough J., Bai W., B. Ruijsink, Puyol-Anton E., Cruz G., Prieto C., King A.P. Schnabel J.A. (2019) High-quality segmentation of low quality cardiac MR images using k-space artefact correction. Proceedings of Machine Learning Research 102:380–389. http://proceedings.mlr.press/v102/oksuz19a.html

Clough J. R., Oksuz I., Byrne N., Schnabel J. A. and King A. P. (2019). Explicit topological priors for deep-learning based image segmentation using persistent homology. In: Chung A., Gee J., Yushkevich P., Bao S. (eds) Information Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science, vol 11492. Springer, Cham. https://doi.org/10.1007/978-3-030-20351-1_2

Chen C., Bai W. and Rueckert D. (2019) Multi-task Learning for Left Atrial Segmentation on GE-MRI. In: Pop M. et al. (eds) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science, vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_32

Kerfoot E., Clough J., Oksuz, I.,Lee J., King A.P., Schnabel J.,  Left-Ventricle Quantification Using Residual U-Net. In: Pop M. et al. (eds) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science, vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_40

Bai W., Sinclair M., Tarroni G., Oktay O., Rajchl M., Vaillant G., Lee A.M. … and Rueckert D. (2018) Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J Cardiovasc Magn Reson. 2018 Sep 14;20(1):65. PMID: 30217194 PMCID: PMC6138894  doi: 10.1186/s12968-018-0471-x.

Oksuz, I., Clough J., Puyol-Anton E., Bustin A., Cruz G., Prieto C., Botnar R., Rueckert D., Schnabel J., King A.P. (2018) Cardiac MR Motion Artefact Correction from K-space Using Deep Learning-Based Reconstruction. In: Knoll F., Maier A., Rueckert D. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2018. Lecture Notes in Computer Science, vol 11074. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-00129-2_3

Schlemper J., Castro D., Bai W., Qin C., Oktay O., Duan J., Price A., Hajnal J. and Rueckert D. (2018). Bayesian Deep Learning for Accelerated MR Image Reconstruction. In: Knoll F., Maier A., Rueckert D. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2018. Lecture Notes in Computer Science, vol 11074. Springer, Cham. https://doi.org/10.1007/978-3-030-00129-2_8

Qin C., Bai W., Schlemper J., Petersen S.E., Piechnik S.K., Neubauer S. and Rueckert D. (2018). Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image. In: Knoll F., Maier A., Rueckert D. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2018. Lecture Notes in Computer Science, vol 11074. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-00129-2_7

Robinson R., Oktay O., Bai W., Valindria V. V., Sanghvi M. M., Aung N., Paiva J. M., Zemrak F., Fung K., Lukaschuk E., Lee A. M., Carapella V., Kim Y. J., Kainz B., Piechnik S. K., Neubauer S., Petersen S. E., Page C., Rueckert D. and Glocker B. (2018) Real-Time Prediction of Segmentation Quality. In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11073. Springer, Cham.  https://arxiv.org/pdf/1806.06244.pdf

Seitzer M., Yang G., Schlemper J., Oktay O., Wurfl T., Christlein V., Wong T., Mohiad R., Firmin D., Keegan J., Rueckert D. and Maier A. (2018) Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction. Medical Image Computing and Computer Assisted Intervention (MICCAI) 2018, accepted. Preprint available at https://arxiv.org/pdf/1806.11216

Schlemper J., Yang G., Ferreira P., Scott A., McGill L., Khalique Z., Gorodezky M., Roehl M., Keegan J., Pennell D., Firmin D. and Rueckert D. (2018) Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI. In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11070. Springer, Cham. https://doi.org/10.1007/978-3-030-00928-1_34

Schlemper J., Oktay O., Bai W., Castro D., Duan J., Qin C., Hajnal J. and Rueckert D. (2018) Cardiac MR Segmentation from Undersampled k-space using Deep Latent Representation Learning. In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11070. Springer, Cham. https://doi.org/10.1007/978-3-030-00928-1_30 DOI: 10.1007/978-3-030-00928-1_30

Qin C., Bai W., Schlemper J., Petersen S.E., Piechnik S.K., Neubauer S., and Rueckert D. (2018). Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences. In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11071. Springer, Cham. https://doi.org/10.1007/978-3-030-00934-2_53

Oksuz I., Ruijsink B., Puyol-Anton E., Bustin A., Cruz G., Prieto C., Rueckert D., Schnabel J.A. and King A.P. (2018). Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection. In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11070. Springer, Cham. https://doi.org/10.1007/978-3-030-00928-1_29.

Bai W., Suzuki H., Qin C., Tarroni G., Oktay O., Matthews P.M. and Rueckert D. (2018). Recurrent neural networks for aortic image sequence segmentation with sparse annotations.In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11073. Springer, Cham. https://doi.org/10.1007/978-3-030-00937-3_67

Lopez K., Radhouene N., Mukherjee R., Rashid I., Razavi R., Prieto C., Roujol S. and Botnar R. (2018). Contrast-free 3D whole-heart magnetization transfer imaging for simultaneous myocardial scar and cardiac vein visualization. Proc. Intl. Soc. Mag. Reson. Med. 26 (2018). Available at http://indexsmart.mirasmart.com/ISMRM2018/PDFfiles/4890.html

Bustin A., Neji R., Ginami G., Ismail T. F., Rashid I., Botnar R. M. and Prieto C. (2018) Four-minute whole-heart coronary MRA with sub-millimeter isotropic resolution and 100% respiratory scan efficiency. Proceedings of the 5th Meeting of the EUROCMR / SCMR, doi: 10.1002/mrm.27354.

Puyol-Anton E., Ruijsink B., Langet H.,De Craene M., Schnabel J. A., Piro P., King A. P. and Sinclair M. (2018) Fully automated myocardial strain estimation from cine MRI using convolutional neural networks. IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA, 2018, pp. 1139-1143. doi: 10.1109/ISBI.2018.8363772

Oksuz I., Ruijsink B., Puyol-Anton E., Sinclair M., Rueckert D., Schnabel J. A. and King A.P. (2018) Automatic Left Ventricular Outflow Tract Classification for Accurate Cardiac MR Planning. IEEE IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, 2018, pp. 462-465. doi: 10.1109/ISBI.2018.8363616

Lopez K., Neji R., Mukherjee R., Whitaker J., Razavi R., Muñoz C., Prieto C., Roujol S. and Botnar R.M. (2017). Non-contrast free breathing and motion corrected 3D whole heart quantitative magnetization transfer imaging for assessment of myocardial fibrosis. Proc. Intl. Soc. Mag. Reson. Med. 25. Available at http://indexsmart.mirasmart.com/ISMRM2017/PDFfiles/0046.html

Bai W., Oktay O., Sinclair M., Suzuki H., Rajchl M., Tarroni G., Glocker B., King A., Matthews P. M. and Rueckert D. (2017) Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation. In: Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D., Duchesne S. (eds) Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science, vol. 10434:253-260. Springer, Cham. doi: 10.1007/978-3-319-66185-8_29

Robinson R., Valindria V. V., Bai W., Suzuki H., Matthews P.M., Page C., Rueckert D., and Glocker B. (2017) Automatic Quality Control of Cardiac MRI Segmentation in Large-Scale Population Imaging. In: Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D., Duchesne S. (eds) Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science, vol 10433:720-727. Springer, Cham. doi: 10.1007/978-3-319-66182-7_82

Conference Abstracts

Jaubert O., Cruz G., Bustin A., Schneider T., Rueckert D., Botnar R.M. and Prieto C. Dixon-CMRF: cardiac tissue characterization using three point Dixon MR fingerprinting. SCMR 2019, Seattle.

Jaubert O., Cruz G., Bustin A., Schneider T., Koken P., Doneva M., Rueckert D., Botnar R.M. and Prieto C. MORE-MRF: Motion Resolved cardiac multi parametric mapping using fingerprinting. SCMR 2019, Seattle.

Bustin A., Correia T., Rashid I., Cruz G., Neji R., Botnar R.M. and Prieto C. Isotropic sub-millimeter CMRA: combining high undersampling with non-rigid motion correction. SCMR 2019, Seattle.

Bustin A., Cruz G., Jaubert O., Lopez K., Botnar R.M. and Prieto C. High-dimensionality undersampled patch-based reconstruction (HD-PROST) for accelerated multi-contrast magnetic resonance imaging.  ISMRM 2019, Montreal.

Bustin A., Correia T., Rashid I., Cruz G., Neji R., Botnar R.M. and Prieto C. Highly accelerated 3D whole-heart isotropic sub-millimeter CMRA with non-rigid motion correction. ISMRM 2019, Montreal.

Bustin A., Milotta G., Neji R., Botnar R.M. and Prieto C. Fast and accurate free-breathing whole-heart 3D T2 mapping. ISMRM 2019, Montreal.

Fuin, N., Bustin A., Kuestner T., Botnar R.M. and Prieto C. A Variational Neural Network for Whole-Heart Coronary MRA Reconstruction. SCMR/ISMRM Co-provided Workshop 2019, Seattle.

Milotta G., Ginami G., Neji R., Prieto C. and Botnar R.M. Simultaneous 3D whole-heart bright-blood and black blood imaging for cardiovascular anatomy and wall assessment with interleaved T2prep-IR. SMRA 2018, Glasgow, UK.

Ginami G. et al., Non-contrast enhanced simultaneous bright- and black-blood 3D whole-heart MRI in patients with congenital heart disease. SMRA 2018, Glasgow, UK.

Correia T., Rashid I., Ginami G., Cruz G., Neji R., Botnar R.M. and Prieto C.. XD-ORCCA: Optimized respiratory-resolved 3D Cartesian coronary MRA. SMRA 2018, Glasgow, UK.

Milotta G., Ginami G., Neji R., Prieto C. and Botnar R.M. Simultaneous 3D whole-heart bright-blood and black blood imaging for cardiovascular anatomy and wall assessment with interleaved T2prep-IR. ISMRM 2018, Paris, France.

Cruz G., Jaubert O., Malik S., Schneider T., Botnar R.M. and Prieto C. Rigid motion corrected low rank magnetic resonance fingerprinting. ISMRM 2018, Paris, France.

Cruz G., Botnar R.M. and Prieto C. Zero Dimensional Self Navigated Autofocus for Motion Corrected Magnetic Resonance Fingerprinting. ISMRM 2018, Paris, France.

Cruz G., Bustin A., Jaubert O., Schneider T., Botnar R.M. and Prieto C. Locally Low Rank Regularization for Magnetic Resonance Fingerprinting. ISMRM 2018, Paris, France.

Bustin A., Ginami G., Correia T., Ismail T.F., Neji R., Botnar R.M. and Prieto C.. Whole-heart coronary MRA with sub-millimeter isotropic resolution in four-minute acquisition. ISMRM 2018, Paris, France.

Bustin A., Cruz G., Ginami G., Correia T., Rashid I., Neji R., Botnar R.M. and Prieto C. 3D-patch based low-rank reconstruction (PROST) for highly-accelerated CMRA acquistion. ISMRM 2018, Paris, France.

Jaubert O., Cruz G., Schneider T., Rueckert D., Botnar R.M. and Prieto C. MORE-MRF: Towards Motion Resolved Cardiac Multi-Parametric Mapping with Magnetic Resonance Fingerprinting. ISMRM 2018, Paris, France.

Correia T., Cruz G., Ginami G., Neji R., Botnar R.M. and Prieto C. Assessment of respiratory motion-resolved and nonrigid motion-corrected 3D Cartesian coronary MRA. ISMRM 2018, Paris, France.

Correia T., Ginami G., Neji R., Cruz G., Botnar R.M. and Prieto C. Optimized respiratory-resolved motion-compensated 3D Cartesian coronary MRA. ISMRM 2018, Paris, France.

Bustin A., Ginami G., Rashid I., Correia T., Ismail T.F., Neji R., Botnar R.M. and Prieto C.. Highly-accelerated whole-heart 3D CMRA with sub-millimeter isotropic resolution and 3D-PROST reconstruction. BISMRM 2018, London, UK.

Oksuz, I. Automatic mis-triggering artefact detection for image quality assessment of cardiac MRI. BSCI 2018, Edinburgh, UK.

Milotta G., Ginami G., Neji R., Prieto C. and Botnar R.M. 3D whole-heart free-breathing coronary lumen and vessel wall imaging with interleaved T2prep-IR. CMR 2018, Barcelona, Spain.

Mukherjee R.K., Whitaker J., Roujol S., Ginami G., Neji R., Villa A., Chubb H., O’Neill L., Williams S.E., Silberbauer J., Wright M., O’Neill M., Botnar R.M., Prieto C. and Razavi R. 3D High resolution imaging of ventricular scar: head-to-head comparison of three late gadolinium enhancement (LGE) sequences in a porcine infarct model at 1.5T. CMR 2018, Barcelona, Spain.

Nordio G., Bustin A., Odille F, Prieto C. and Botnar R.M. 3D SASHA myocardial T1 mapping with high accuracy and improved precision. CMR 2018, Barcelona, Spain.

Milotta G., Ginami G., Prieto C. and Botnar R.M. 3D whole-heart free-breathing coronary lumen and vessel wall imaging with interleaved T2prep-IR. CMR 2018, Barcelona, Spain.

Bustin A., Neji R., Ginami G., Ismail T., Rashid I, Botnar R.M. and Prieto C.. Four-minute whole-heart coronary MRA with sub-millimeter isotropic resolution and 100% respiratory scan efficiency. CMR 2018, Barcelona, Spain.

Ginami G., Neji R., Lopez K., Roujol S., Mountney P., Razavi R., Botnar R.M. and Prieto C.. Simultaneous 3D Whole-Heart Bright-Blood Visualization of the Coronary Sinus and Heart Anatomy and Black-Blood PSIR Depiction of Atrial Walls for Non-Contrast Enhanced Interventional Planning. CMR 2018, Barcelona, Spain.

Selected relevant publications

Puyol-Antón E, Sinclair M, Gerber B, Amzulescu M, Langet H, De Craene M, Aljabar P, Schnabel J, Piro P, King A (2018) Multiview Machine Learning Using an Atlas of Cardiac Cycle Motion. In: Pop M. et al. (eds) Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. STACOM 2017. Lecture Notes in Computer Science, vol 10663. Springer, Cham. https://doi.org/10.1007/978-3-319-75541-0_1

Puyol-Antón E., Langet H., De Craene M., Piro P., Schnabel J.A. and King A.P. (2018) Learning Associations Between Clinical Information and Motion-based Descriptors Using a Large Scale MR-derived Cardiac Motion Atlas, Proceedings MICCAI STACOM, 2018.  arXiv:1807.10653

Henningsson M., Smink J., van Ensbergen G. and Botnar R. (2017) Coronary MR angiography using image-based respiratory motion compensation with inline correction and fixed gating efficiency. Magn Reson Med. 2017 Mar 20. doi: 10.1002/mrm.26678. PubMed PMID: 28321900.

Nordio G., Henningsson M., Chiribiri A., Villa A.D., Schneider T. and Botnar R.M. (2017) 3D myocardial T1 mapping using saturation recovery. J Magn Reson Imaging. 2017 Feb 2. doi: 10.1002/jmri.25575. PubMed PMID: 28152227.

Aung N., Sanghvi M.M., Zemrak F., Fung K., Paiva J.M., Francis J.M., Khanji M.Y., Lukaschuk E., Lee A.M., Carapella V., Kim Y.J., Leeson P., Piechnik S.K. and Neubauer S. (2017) Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort, J Cardiovasc Magn Reson, doi: 10.1186/s12968-017-0327-9. PubMed PMID: 28178995. PubMed Central PMCID: PMC5304550.

Cruz G., Atkinson D., Henningsson M., Botnar R.M. and Prieto C. (2016) Highly efficient nonrigid motion-corrected 3D whole-heart coronary vessel wall imaging. Magn Reson Med. 2016 May 25. doi: 10.1002/mrm.26274.

Petersen S.E., Matthews P.M., Francis J.M., Robson M.D., Zemrak F., Boubertakh R., Young A.A., Hudson S., Weale P., Garratt S., Collins R., Piechnik S. and Neubauer S. (2016) UK Biobank’s cardiovascular magnetic resonance protocol. J Cardiovasc Magn Reson. 2016 Feb 1;18:8. doi: 10.1186/s12968-016-0227-4. PubMed PMID: 26830817; PubMed Central PMCID: PMC4736703.

Gomez, A., Oktay, O., Rueckert, D., Penney, G. P., Schnabel, J. A., Simpson, J. M. & Pushparajah, K. (2016) Regional Differences in End-Diastolic Volumes between 3D Echo and CMR in HLHS Patients. Frontiers in Pediatrics 4:133. doi: 10.3389/fped.2016.00133. PubMed Central PMCID: PMC5152531.

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