PURPOSE: A software assistant for automatic evaluation of CT-angiograms (CTA) was developed. It should enable the visualization of the vessel lumen and the quantitative evaluation of a stenosis. CTA examinations of patients with suspected carotid artery stenoses were used for the evaluation of the software assistant. MATERIALS AND METHODS: Twelve Patients with suspected high-grade stenosis of the carotid arteries underwent a CTA examination using a multislice CT scanner. The data were analyzed and evaluated using the new software assistant. The results were compared with the data of digital subtraction angiography (DSA) of these patients. RESULTS: The time of digital postprocessing with the new software-assistant took about six minutes on average. Contour extraction of the vessel, MIP and curved MPR (c-MPR) and orthogonal cross-sectional images of the vessels were calculated, followed by an automatic quantification of stenosis by the use of the c-MPR. A good correlation was found between CTA and DSA data regarding the stenosis grade (r = 0.82). Furthermore, some information could be provided about the plaque morphology. CONCLUSION: The software-assisted detection and analysis of carotid artery stenosis with the new developed program is possible within a justifiable time. DSA- and CTA-data did not show a significant difference in stenosis grading. Further development of software tools could lead to a better characterization of plaque morphology.
PURPOSE: A software assistant for automatic evaluation of CT-angiograms (CTA) was developed. It should enable the visualization of the vessel lumen and the quantitative evaluation of a stenosis. CTA examinations of patients with suspected carotid artery stenoses were used for the evaluation of the software assistant. MATERIALS AND METHODS: Twelve Patients with suspected high-grade stenosis of the carotid arteries underwent a CTA examination using a multislice CT scanner. The data were analyzed and evaluated using the new software assistant. The results were compared with the data of digital subtraction angiography (DSA) of these patients. RESULTS: The time of digital postprocessing with the new software-assistant took about six minutes on average. Contour extraction of the vessel, MIP and curved MPR (c-MPR) and orthogonal cross-sectional images of the vessels were calculated, followed by an automatic quantification of stenosis by the use of the c-MPR. A good correlation was found between CTA and DSA data regarding the stenosis grade (r = 0.82). Furthermore, some information could be provided about the plaque morphology. CONCLUSION: The software-assisted detection and analysis of carotid artery stenosis with the new developed program is possible within a justifiable time. DSA- and CTA-data did not show a significant difference in stenosis grading. Further development of software tools could lead to a better characterization of plaque morphology.