Matsui, H., Germanakis, I., Kulinskaya, E. and Gardiner, H. M. (2011) Temporal and spatial performance of vector velocity imaging in the human fetal heart. Ultrasound in Obstetrics and Gynecology, 37 (2). pp. 150-157. ISSN 0960-7692
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Objectives: To assess the spatial and temporal performance of fetal myocardial speckle tracking, using high-frame-rate (HFR) storing and Lagrangian strain analysis. Methods: Dummy electrocardiographic signaling permitted DICOM HFR in 124 normal fetuses and paired low-frame-rate (LFR) video storing at 25 Hz in 93 of them. Vector velocity imaging (VVI) tracking co-ordinates were used to compare time and spatial domain measures. We compared tracking success, Lagrangian strain, peak diastolic velocity and positive strain rate values in HFR vs. LFR video storing. Further comparisons within an HFR subset included Lagrangian vs. natural strain, VVI vs. M-mode annular displacement, and VVI vs. pulsed-wave tissue Doppler imaging (TDI) peak velocities. Results: HFR (average 79.4 Hz) tracking was more successful than LFR (86 vs. 76%, P = 0.024). Lagrangian and natural HFR strain correlated highly (left ventricle (LV): r = 0.883, P < 0.001; right ventricle (RV): r = 0.792, P < 0.001) but natural strain gave 20% lower values, suggesting reduced reliability of measurement. Lagrangian HFR strain was similar in LV and RV and decreased with gestation (P = 0.015 and P < 0.001, respectively). LV Lagrangian LFR strain was significantly lower than the values for the RV (P < 0.001) and those using paired LV-HFR recordings (P = 0.007). Annular displacement methods correlated highly (LV = 1.046, r = 0.90, P < 0.001; RV = 1.170, r = 0.88, P < 0.001). Early diastolic waves were visible in 95% of TDI, but in only 26% of HFR and 0% of LFR recordings, and HFR-VVI velocities were significantly lower than those for TDI (P < 0.001). Conclusions: Doppler estimation of velocities remains superior to VVI but image gating and use of original co-ordinates should improve offline VVI assessment of fetal myocardial function.
Item Type: | Article |
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Faculty \ School: | Faculty of Science > School of Computing Sciences |
UEA Research Groups: | Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Science > Research Groups > Data Science and Statistics Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023) |
Depositing User: | Vishal Gautam |
Date Deposited: | 16 Mar 2011 08:48 |
Last Modified: | 24 Jan 2024 01:19 |
URI: | https://ueaeprints.uea.ac.uk/id/eprint/21697 |
DOI: | 10.1002/uog.8815 |
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