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Proceedings Paper

Perfusion deficit versus anatomic visualization in detection of pulmonary emboli via electron-beam CT: validation in swine
Author(s): Eric A. Hoffman; Jehangir K. Tajik; Greg Petersen; Timmothy J. Reiners; Brad H. Thompson; William Stanford
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Paper Abstract

We present here our initial findings regarding the utility of functional x-ray CT imaging in determining the presence of pulmonary emboli. Recently, x-ray CT has been reported to be a promising technique in detecting pulmonary emboli through direct visualization of the clot as a filling defect of the reconstructed vascular lumen with CT scanning occurring during i.v. contrast drip. To determine whether functional imaging via the dynamic mode of electron beam CT might add to the sensitivity and specificity of pulmonary emboli diagnosis through the visualization of pulmonary parenchymal blood flow and its associated temporal parameters, we scanned 32 pigs and report here our findings on 17 pigs evaluated to date. Findings to date show that the evaluation of flow deficits detected via electron beam CT with a small 2-3 sec. bolus contrast injection has the potential to provide improvement in embolus detection over visual inspection of this section CT/continuous infusion contrast where the viewer is looking for unenhanced regions in the pulmonary arteries.

Paper Details

Date Published: 24 May 1995
PDF: 11 pages
Proc. SPIE 2433, Medical Imaging 1995: Physiology and Function from Multidimensional Images, (24 May 1995); doi: 10.1117/12.209699
Show Author Affiliations
Eric A. Hoffman, Univ. of Iowa College of Medicine (United States)
Jehangir K. Tajik, Univ. of Iowa College of Medicine (United States)
Greg Petersen, Univ. of Iowa College of Medicine (United States)
Timmothy J. Reiners, Univ. of Iowa College of Medicine (United States)
Brad H. Thompson, Univ. of Iowa College of Medicine (United States)
William Stanford, Univ. of Iowa College of Medicine (United States)


Published in SPIE Proceedings Vol. 2433:
Medical Imaging 1995: Physiology and Function from Multidimensional Images
Eric A. Hoffman, Editor(s)

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