Real-time 3D fusion echocardiography

Szmigielski C, Rajpoot K, Grau V, Myerson SG, Holloway C, Noble JA, Kerber R, Becher H

JACC Cardiovasc Imaging 2010 Jul;3(7):682-90

PMID: 20633845


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.