Intracardiac echocardiographic measurement of left ventricular volume

C. Ding, L. Rao, Sherif Nagueh, D. S. Khoury

Research output: Contribution to journalConference article

1 Scopus citations

Abstract

We tested the utility of intracardiac echocardiography (ICE) in measuring left ventricular (LV) volume. In 4 normal dogs, a 10-F percutaneous sheath was placed inside the LV along its major axis. An ICE catheter (9 F, 9 MHz) was then inserted through the sheath into the LV. The ICE catheter was pulled back in 1-mm intervals starting from the apex, and 2-D tomographic images were continuously acquired. Subsequently, the ICE catheter was replaced in the LV by a conductance catheter to measure single-beat volume signals. Stroke volume was determined by thermodilution for validation. All measurements were made in each dog while pacing the atrium at two different cycle lengths (range=300-500 ms). The endocardium was segmented in the ICE images throughout the cardiac cycle, and LV volume was computed by integrating multiple segments (range=55-70 mm). We found that ICE accurately reconstructed LV 3-D anatomy. Stroke volume by ICE was in excellent agreement with thermodilution (error=3.8±3.0%, r=0.99, n=8). Morphology of LV volume signals correlated well with instantaneous volume signals derived by conductance (r=0.93, n=8). In conclusion, ICE accurately reconstructs LV anatomy and volume throughout the cardiac cycle in the normal heart. This approach could facilitate interventional diagnostic and therapeutic procedures.

Original languageEnglish (US)
Pages (from-to)3662-3665
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume26 V
StatePublished - Dec 1 2004
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: Sep 1 2004Sep 5 2004

Keywords

  • Echocardiography
  • Hemodynamics
  • Imaging

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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