4.0 Article

Reproducibility of left ventricular blood flow kinetic energy measured by four-dimensional flow CMR

Journal

BMC RESEARCH NOTES
Volume 14, Issue 1, Pages -

Publisher

SPRINGERNATURE
DOI: 10.1186/s13104-021-05697-3

Keywords

4D flow CMR; Haemodynamics; Flow quantification; Reproducibility analysis

Funding

  1. British Heart Foundation [FS/10/62/28409]
  2. Dutch ZonMw [104003001]
  3. Wellcome Trust [220703/Z/20/Z, 215799/Z/19/Z]

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Four-dimensional flow CMR allows for a comprehensive assessment of blood flow kinetic energy in the heart's ventricles, offering superior reproducibility compared to standard two-dimensional image acquisition. This study evaluated the inter-observer and intra-observer reproducibility of blood flow kinetic energy assessment using 4D flow in 20 healthy volunteers across two centers. The data includes global kinetic energy, systolic and diastolic components, as well as early and late diastolic filling, and can support further research by allowing for larger sample sizes for statistical power analysis of these variables.
Objectives Four-dimensional flow CMR allows for a comprehensive assessment of the blood flow kinetic energy of the ventricles of the heart. In comparison to standard two-dimensional image acquisition, 4D flow CMR is felt to offer superior reproducibility, which is important when repeated examinations may be required. The objective was to evaluate the inter-observer and intra-observer reproducibility of blood flow kinetic energy assessment using 4D flow of the left ventricle in 20 healthy volunteers across two centres in the United Kingdom and the Netherlands. Data description This dataset contains 4D flow CMR blood flow kinetic energy data for 20 healthy volunteers with no known cardiovascular disease. Presented is kinetic energy data for the entire cardiac cycle (global), the systolic and diastolic components, in addition to blood flow kinetic energy for both early and late diastolic filling. This data is available for reuse and would be valuable in supporting other research, such as allowing for larger sample sizes with more statistical power for further analysis of these variables.

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