3D echocardiography: reconstruction algorithm and diagnostic performance of resulting images

Marek Belohlavek; David A. Foley; James B. Seward; James F. Greenleaf

doi:10.1117/12.185230

9 September 1994 Three-dimensional (3D) echocardiography: reconstruction algorithm and diagnostic performance of resulting images

Marek Belohlavek, David A. Foley, James B. Seward, James F. Greenleaf

Author Affiliations +

Proceedings Volume 2359, Visualization in Biomedical Computing 1994; (1994) https://doi.org/10.1117/12.185230
Event: Visualization in Biomedical Computing 1994, 1994, Rochester, MN, United States

Abstract

To date, 3D ultrasound imaging has been hampered by fractionated, job specific computer procedures, and the need for significant operator interaction. This paper presents our data processing algorithm for cardiac structure visualization from serial transesophageal echocardiographic (TEE) images. Major steps in the algorithm are: 1) image registration, 2) histogram operations for contrast enhancement, 3) noise and speckle filtering, 4) segmentation of composite color Doppler flow images, and 5) coordinate system conversion and interpolation. Three-dimensional reconstructions of clinical TEE examinations were compared to the corresponding serial 2D scans using receiver operator characteristics (ROC) analysis. The results demonstrated significantly better trade-off between diagnostic sensitivity and specificity in the 3D method when compared to the original 2D tomograms. We conclude that 3D echocaridography based on our algorithm is clinically feasible. Moreover, the ROC analysis on a limited group of patients indicated that 3D imaging facilitated comprehension of complex anatomic relationships and diagnostic capabilities of conventional 2D TEE echocardiography.

Citation Download Citation

Marek Belohlavek, David A. Foley, James B. Seward, and James F. Greenleaf "Three-dimensional (3D) echocardiography: reconstruction algorithm and diagnostic performance of resulting images", Proc. SPIE 2359, Visualization in Biomedical Computing 1994, (9 September 1994); https://doi.org/10.1117/12.185230

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