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Comparative Study
. 2010 Jul;3(7):682-90.
doi: 10.1016/j.jcmg.2010.03.010.

Real-time 3D fusion echocardiography

Cezary Szmigielski  1 , Kashif Rajpoot, Vicente Grau, Saul G Myerson, Cameron Holloway, J Alison Noble, Richard Kerber, Harald Becher
Affiliations
Free article
Comparative Study

Real-time 3D fusion echocardiography

Cezary Szmigielski et al. JACC Cardiovasc Imaging. 2010 Jul.
Free article

Erratum in

  • JACC Cardiovasc Imaging. 2011 Mar;4(3):314

Abstract

Objectives: This study assessed 3-dimensional fusion echocardiography (3DFE), combining several real-time 3-dimensional echocardiography (RT3DE) full volumes from different transducer positions, for improvement in quality and completeness of the reconstructed image.

Background: The RT3DE technique has limited image quality and completeness of datasets even with current matrix transducers.

Methods: RT3DE datasets were acquired in 32 participants (mean age 33.7 +/- 18.8 years; 27 men, 5 women). The 3DFE technique was also performed on a cardiac phantom. The endocardial border definition of RT3DE and 3DFE segments was graded for quality: good (2), intermediate (1), poor (0), or out of sector. Short-axis and apical images were compared in RT3DE and 3DFE, yielding 2,048 segments. The images were processed to generate 3DFE and then compared with cardiac magnetic resonance.

Results: In the heart phantom, fused datasets showed improved contrast to noise ratio from 49.4 +/- 25.1 (single dataset) to 125.4 +/- 25.1 (6 datasets fused together). In subjects, more segments were graded as good quality with 3DFE (805 vs. 435; p < 0.0001) and fewer as intermediate (184 vs. 283; p = 0.017), poor (31 vs. 265; p < 0.0001), or out of sector (4 vs. 41; p < 0.001) compared with the single 3-dimensional dataset. End-diastolic volume (EDV) and end-systolic volume (ESV) obtained from 3-dimensional fused datasets were equivalent to those from single datasets (EDV 118.2 +/- 39 ml vs. 119.7 +/- 43 ml; p = 0.41; ESV 48.1 +/- 30 ml vs. 48.4 +/- 35 ml; p = 0.87; ejection fraction [EF] 61.0 +/- 10% vs. 61.8 +/- 10%; p = 0.44). Bland-Altman analysis showed good 95% limits of agreement for the nonfused datasets (EDV +/-46 ml; ESV +/-36 ml; EF +/-14%) and the fused datasets (EDV +/-45 ml; ESV +/-35 ml; EF +/-16%), when compared with cardiac magnetic resonance.

Conclusions: Fusion of full-volume datasets resulted in an improvement in endocardial borders, image quality, and completeness of the datasets.

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