UK Biobank's cardiovascular magnetic resonance protocol
Open accessAbstract
BACKGROUND: UK Biobank's ambitious aim is to perform cardiovascular magnetic resonance (CMR) in 100,000 people previously recruited into this prospective cohort study of half a million 40-69 year-olds. METHODS/DESIGN: We describe the CMR protocol applied in UK Biobank's pilot phase, which will be extended into the main phase with three centres using the same equipment and protocols. The CMR protocol includes white blood CMR (sagittal anatomy, coronary and transverse anatomy), cine CMR (long axis cines, short axis cines of the ventricles, coronal LVOT cine), strain CMR (tagging), flow CMR (aortic valve flow) and parametric CMR (native T1 map). DISCUSSION: This report will serve as a reference to researchers intending to use the UK Biobank resource or to replicate the UK Biobank cardiovascular magnetic resonance protocol in different settings.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Standard clinical acquisition protocols in 3D-US and CMR still have limitations in visualising the underlying anatomy due to imaging artefacts or operator-dependent errors.
“Some of the standard clinical acquisition protocols in 3DUS and CMR still have limitations in visualising the underlying anatomy due to imaging artefacts ( e.g. cardiac motion, low slice resolution, lack of slice coverage [35]) or operatordependent errors ( e.g. shadows, signal drop-outs).”
From Anatomically constrained neural networks (ACNNS): application to cardiac image enhancement and segmentation (2018) · cites this paper as [35]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cited by
Sorted by citations
Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.-A., Cetin, I., Lekadir, K., Camara, O., Gonzalez Ballester, M. A., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2837502 · Cited by 2,431 · see citing works
Self-citationLittlejohns, T. J., Holliday, J., Gibson, L. M., Garratt, S., Oesingmann, N., Alfaro-Almagro, F., Bell, J. D., Boultwood, C., Collins, R., Conroy, M. C., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15948-9 · Cited by 812 · see citing works
Self-citationOktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Cook, S. A., de Marvao, A., Dawes, T., O'Regan, D. P., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2743464 · Cited by 751 · see citing works
“Similar problems have been reported in large cohort studies such as the UK Biobank [35], which leads to inaccurate quantitative measurements or even the discarding of acquired images.”
BackgroundResultUK Biobank: Opportunities for cardiovascular research
Littlejohns, T. J., Sudlow, C., Allen, N. E., & Collins, R. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehx254 · Cited by 270 · see citing works
Self-citationPirruccello, J. P., Bick, A., Wang, M., Chaffin, M., Friedman, S., Yao, J., Guo, X., Venkatesh, B. A., Taylor, K. D., Post, W. S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15823-7 · Cited by 267 · see citing works
Duan, J., Bello, G., Schlemper, J., Bai, W., Dawes, T. J. W., Biffi, C., de Marvao, A., Doumoud, G., O'Regan, D. P., & Rueckert, D. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2894322 · Cited by 227 · see citing works
Deep learning enables genetic analysis of the human thoracic aorta
Pirruccello, J. P., Chaffin, M. D., Chou, E. L., Fleming, S. J., Lin, H., Nekoui, M., Khurshid, S., Friedman, S. F., Bick, A. G., Arduini, A., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00962-4 · Cited by 210 · see citing works
Cross-modal autoencoder framework learns holistic representations of cardiovascular state
Radhakrishnan, A., Friedman, S. F., Khurshid, S., Ng, K., Batra, P., Lubitz, S. A., Philippakis, A. A., & Uhler, C. (2023) · Nature Communications · doi:10.1038/s41467-023-38125-0 · Cited by 82 · see citing works
“All models were optimized with the Adam optimizer 65 To avoid any data leakage the entire test set was made of samples with MRI phenotypes labelled in 39 , ensuring a fair comparison with models trained on downstream prediction tasks.”
Puyol-Antón, E., Ruijsink, B., Mariscal Harana, J., Piechnik, S. K., Neubauer, S., Petersen, S. E., Razavi, R., Chowienczyk, P., & King, A. P. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.859310 · Cited by 85 · see citing works
Self-citationRämö, J. T., Jurgens, S. J., Kany, S., Choi, S. H., Wang, X., Smirnov, A. N., Friedman, S. F., Maddah, M., Khurshid, S., Ellinor, P. T., et al. (2024) · Circulation · doi:10.1161/circulationaha.124.070982 · Cited by 26 · see citing works
- Authors
- 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., & Neubauer, S.
- Affiliations
- Queen Mary University of London (GB)
- Imperial College London (GB)
- University of Oxford (GB)
- University of Auckland (NZ)
- UK Biobank (GB)
- Corresponding author
- Petersen, Steffen E.
- ORCID
- Venue
- Journal of Cardiovascular Magnetic Resonance
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 18
- Issue
- 1
- Pages
- 8
- ISSN
- 1097-6647 (ISSN-L)
- PMC
- PMC4736703
- OpenAlex
- W2258978862
- Cited by
- 481
- References
- 0
- Citations by year
- 2026: 62 · 2025: 76 · 2024: 66 · 2023: 63 · 2022: 48 · 2021: 49 · 2020: 35 · 2019: 37 · 2018: 20 · 2017: 13 · 2016: 10 · 2015: 1
- FWCI
- 23.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Cardiovascular Magnetic Resonance (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
- ResearchSpace (University of Auckland) (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- Wellcome Trust (093707)
- National Institute for Health and Care Research (NF-SI-0512-10005, NF-SI-0514-10022)
- British Heart Foundation (PG/14/89/31194)
- Imperial College London
- National Institutes of Health
- Medical Research Council (MR/M024903/1, PG/14/89/31194, MC_PC_15067)
- National Heart, Lung, and Blood Institute (R01HL121754)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Function and Risk Factors
- Keywords
- cardiovascular magnetic resonance, UK Biobank, aortic valve flow, T1 mapping, coronary anatomy, left ventricle, prospective cohort, cardiovascular imaging
- MeSH terms
- Adult, Aged, Clinical Protocols, Female, United Kingdom, Heart Diseases, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Pilot Projects, Predictive Value of Tests, Prognosis, Prospective Studies, Biological Specimen Banks, Magnetic Resonance Imaging, Cine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
