Introduction: The aim of the study was to examine the ability of a 64-slice MDCT to detect in-stent stenoses in an ex vivo model of coronary stents.
Methods: Five different stents (Liberté, Boston Scientific; Driver, Medtronic; Multi-Link Vision, Guidant; Taxus Express, Boston Scientific; Cypher, Cordis) were examined using a dynamic cardiac phantom. The stents were pulled over a vessel model that consists of a polymer tube with diameters of 3.0, 3.5, and 4.0 mm and four different degrees of stenoses (0%; 30%; 50%; 70-80%). This model was moved with a rate of 60 bpm to mimic cardiac motion. To assess the degree of artificial signal reduction (artificial reduction of attenuation (ARA)) by the different stents, attenuation values were measured in the vessel outside the stent, and in the non-stenotic vessel inside the stent. Furthermore the grade of stenosis was assessed by two clinical observers.
Results: Highest ARA was found for the Cypher Stent (35 HU), whereas the Liberté Stent presented the lowest ARA (16 HU). Depending on the stent and the vessel diameter, up to 87.5% of the stenoses were correctly diagnosed. In the 3.0 and 3.5 mm vessels, a nonstenotic or low-grade stenotic vessel was diagnosed as intermediate or high-grade stenosis in 22.5%, whereas in the 4.0 mm vessels, this kind of overestimation did not occur. A 50% stenosis was diagnosed as a 30% stenosis in 30%. On the other hand, high-grade stenoses were underestimated in only 10%. On a four-point scale, the average deviation from the real grade of stenosis was 0.21 for the Liberté stent, 0.54 for the Taxus Express stent, 0.29 for Driver stent, 0.62 for the Multi-Link Vision stent, and 0.37 for the Cypher stent.
Conclusions: In a dynamic cardiac phantom model, high grade stenoses in vessels with a diameter of 4 mm could be reliably detected irrespective of the stent type used in this study. Vice versa, high grade stenoses (> or = 50%) could only be ruled out with certainty in vessels with a diameter of 4 mm. In smaller vessels, the ability to correctly diagnose high-grade stenoses was dependent on the type of stent and the imaging artifacts associated with it.
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http://dx.doi.org/10.1007/s00392-007-0564-2 | DOI Listing |
PLoS One
January 2025
Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States of America.
Complex systems, such as in brains, markets, and societies, exhibit internal dynamics influenced by external factors. Disentangling delayed external effects from internal dynamics within these systems is often difficult. We propose using a Vector Autoregressive model with eXogenous input (VARX) to capture delayed interactions between internal and external variables.
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Center for Aging and Population Health, School of Public Health, University of Pittsburgh, 310 BelPB, 130 N. Bellefield Avenue, Pittsburgh, PA, 15213, USA.
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January 2025
Department of Neurosurgery, Washington University School of Medicine, Springfield, United States.
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Alzheimers Dement
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