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Proceedings Paper

Perfusion visualization and analysis for pulmonary embolism
Author(s): Michael S. Vaz; Atilla P. Kiraly; David P. Naidich; Carol L. Novak
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Paper Abstract

Given the nature of pulmonary embolism (PE), timely and accurate diagnosis is critical. Contrast enhanced high-resolution CT images allow physicians to accurately identify segmental and sub-segmental emboli. However, it is also important to assess the effect of such emboli on the blood flow in the lungs. Expanding upon previous research, we propose a method for 3D visualization of lung perfusion. The proposed method allows users to examine perfusion throughout the entire lung volume at a single glance, with areas of diminished perfusion highlighted so that they are visible independent of the viewing location. This may be particularly valuable for better accuracy in assessing the extent of hemodynamic alterations resulting from pulmonary emboli. The method also facilitates user interaction and may help identify small peripheral sub-segmental emboli otherwise overlooked. 19 patients referred for possible PE were evaluated by CT following the administration of IV contrast media. An experienced thoracic radiologist assessed the 19 datasets with 17 diagnosed as being positive for PE with multiple emboli. Since anomalies in lung perfusion due to PE can alter the distribution of parenchymal densities, we analyzed features collected from histograms of the computed perfusion maps and demonstrate their potential usefulness as a preliminary test to suggest the presence of PE. These histogram features also offer the possibility of distinguishing distinct patterns associated with chronic PE and may even be useful for further characterization of changes in perfusion or overall density resulting from associated conditions such as pneumonia or diffuse lung disease.

Paper Details

Date Published: 14 April 2005
PDF: 11 pages
Proc. SPIE 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images, (14 April 2005); doi: 10.1117/12.595769
Show Author Affiliations
Michael S. Vaz, Barco Medical Imaging Systems (United States)
Atilla P. Kiraly, Siemens Corporate Research (United States)
David P. Naidich, New York Univ. Medical Ctr. (United States)
Carol L. Novak, Siemens Corporate Research (United States)


Published in SPIE Proceedings Vol. 5746:
Medical Imaging 2005: Physiology, Function, and Structure from Medical Images
Amir A. Amini; Armando Manduca, Editor(s)

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