PURPOSE: To determine optimal acquisition parameters and measurement techniques for CT angiography of the carotid bifurcation. METHODS: Anatomic phantoms were created in which the diameter of the carotid artery stenoses ranged from 15% to 95%. Initially, we compared the accuracy of stenosis determination obtained by using various values of section collimation and table pitch. Subsequently, applying the combination of collimation and pitch that yielded the greatest longitudinal coverage without degradation in accuracy, we compared the accuracy of measurements performed with various display algorithms, including axial, magnified axial, maximum intensity projection (MIP), and shaded surface display (SSD) images. Last, we determined the effect on accuracy of varying both window and level settings. The standard of reference for all measurements was considered to be caliper measurements made of the models at the time of their construction. RESULTS: CT angiography was highly accurate for determining the percentage of stenosis; the average difference between CT angiographic measurements and the standard of reference was less than 1% for all parameter combinations and measurement techniques. Precision varied among the measurement techniques. Magnified axial images provided more precise measurements than either the MIP or SSD images. Although there was a trend toward improved precision with the use of magnified versus unmagnified axial images and MIP versus SSD images, neither of these comparisons reached statistical significance. Systematic error was produced by changing the level setting from that halfway between the luminal density and vessel wall density. Random error was introduced by using window settings greater than zero. CONCLUSION: CT angiography was highly accurate and precise for determining percentage of stenosis. The highest precision was attained by using magnified axial images with the level halfway between luminal density and vessel wall density and with the window set to zero.
PURPOSE: To determine optimal acquisition parameters and measurement techniques for CT angiography of the carotid bifurcation. METHODS: Anatomic phantoms were created in which the diameter of the carotid artery stenoses ranged from 15% to 95%. Initially, we compared the accuracy of stenosis determination obtained by using various values of section collimation and table pitch. Subsequently, applying the combination of collimation and pitch that yielded the greatest longitudinal coverage without degradation in accuracy, we compared the accuracy of measurements performed with various display algorithms, including axial, magnified axial, maximum intensity projection (MIP), and shaded surface display (SSD) images. Last, we determined the effect on accuracy of varying both window and level settings. The standard of reference for all measurements was considered to be caliper measurements made of the models at the time of their construction. RESULTS: CT angiography was highly accurate for determining the percentage of stenosis; the average difference between CT angiographic measurements and the standard of reference was less than 1% for all parameter combinations and measurement techniques. Precision varied among the measurement techniques. Magnified axial images provided more precise measurements than either the MIP or SSD images. Although there was a trend toward improved precision with the use of magnified versus unmagnified axial images and MIP versus SSD images, neither of these comparisons reached statistical significance. Systematic error was produced by changing the level setting from that halfway between the luminal density and vessel wall density. Random error was introduced by using window settings greater than zero. CONCLUSION: CT angiography was highly accurate and precise for determining percentage of stenosis. The highest precision was attained by using magnified axial images with the level halfway between luminal density and vessel wall density and with the window set to zero.
Authors: Michael H Lev; Javier M Romero; Daniel N F Goodman; Ranjit Bagga; H Young Kwon Kim; Neil A Clerk; Robert H Ackerman; R Gilberto Gonzalez Journal: AJNR Am J Neuroradiol Date: 2003 Jun-Jul Impact factor: 3.825
Authors: B B Ertl-Wagner; R Bruening; J Blume; R-T Hoffmann; S Mueller-Schunk; B Snyder; M F Reiser Journal: AJNR Am J Neuroradiol Date: 2006-01 Impact factor: 3.825
Authors: Kiran R Nandalur; Erol Baskurt; Klaus D Hagspiel; C Douglas Phillips; Christopher M Kramer Journal: AJR Am J Roentgenol Date: 2005-01 Impact factor: 3.959
Authors: Max Wintermark; Christine Glastonbury; Elizabeth Tong; Benison C Lau; Sarah Schaeffer; Jeffrey D Chien; Peter J Haar; David Saloner Journal: J Neurol Sci Date: 2008-01-29 Impact factor: 3.181
Authors: Jean M U-King-Im; Rikin A Trivedi; Evis Sala; Martin J Graves; Mathew Gaskarth; Nicholas J Higgins; Justin C Cross; William Hollingworth; Richard A Coulden; Peter J Kirkpatrick; Nagui M Antoun; Jonathan H Gillard Journal: Eur Radiol Date: 2004-03-06 Impact factor: 5.315