OBJECTIVE: The objective of this study was to develop a new method of displaying dynamic cerebral computed tomographic (CT) angiography (CTA) data sets in which the time delay to maximum enhancement (Tdelay) is displayed in a range of colors (color-coded CT angiography [cCTA]). MATERIALS AND METHODS: This institutional review board-approved study included multiparametric CT data sets from 16 patients with different types of supra-aortic large vessel occlusions. Color-coded CT angiography was reconstructed from CT perfusion raw data sets. All voxel enhancement curves were fitted to f(t) = α · AIFmtt(t - Δt), with AIFmtt(t), indicating enhancement of AIF dilated by convolution with boxcar function (with mean transit time [mtt]); α, scaling factor; and [INCREMENT]t, transition along the time. The time delay to maximum enhancement was defined as Tdelay = Δt +0.5 · mtt. Values of Tdelay were color-coded and superimposed on temporal maximum intensity projections CTA resulting in colored angiographic composite images. For a pilot clinical evaluation, diagnostic confidence in determining the pathology, quality of the visualization of leptomeningeal collaterals, and additional diagnostic information were assessed. RESULTS: The reconstruction of cCTA was technically feasible in all 16 patients. Both diagnostic confidence (P < 0.01) and the quality of the visualization of collaterals (P < 0.0001) were significantly higher when using the combination of single-phase CTA and cCTA compared with single-phase CTA alone. Additional diagnostic information was obtained with cCTA regarding occlusion type (reader 1: 5 cases and reader 2: 4 cases), differentiation between arteries and veins (11/13), differentiation between antegrade and retrograde filling (12/13), as well as leptomeningeal collateralization (13/14). CONCLUSIONS: Color-coded CT angiography is a technically feasible technique that provides additional information on cerebral hemodynamics in ischemic stroke patients.
OBJECTIVE: The objective of this study was to develop a new method of displaying dynamic cerebral computed tomographic (CT) angiography (CTA) data sets in which the time delay to maximum enhancement (Tdelay) is displayed in a range of colors (color-coded CT angiography [cCTA]). MATERIALS AND METHODS: This institutional review board-approved study included multiparametric CT data sets from 16 patients with different types of supra-aortic large vessel occlusions. Color-coded CT angiography was reconstructed from CT perfusion raw data sets. All voxel enhancement curves were fitted to f(t) = α · AIFmtt(t - Δt), with AIFmtt(t), indicating enhancement of AIF dilated by convolution with boxcar function (with mean transit time [mtt]); α, scaling factor; and [INCREMENT]t, transition along the time. The time delay to maximum enhancement was defined as Tdelay = Δt +0.5 · mtt. Values of Tdelay were color-coded and superimposed on temporal maximum intensity projections CTA resulting in colored angiographic composite images. For a pilot clinical evaluation, diagnostic confidence in determining the pathology, quality of the visualization of leptomeningeal collaterals, and additional diagnostic information were assessed. RESULTS: The reconstruction of cCTA was technically feasible in all 16 patients. Both diagnostic confidence (P < 0.01) and the quality of the visualization of collaterals (P < 0.0001) were significantly higher when using the combination of single-phase CTA and cCTA compared with single-phase CTA alone. Additional diagnostic information was obtained with cCTA regarding occlusion type (reader 1: 5 cases and reader 2: 4 cases), differentiation between arteries and veins (11/13), differentiation between antegrade and retrograde filling (12/13), as well as leptomeningeal collateralization (13/14). CONCLUSIONS: Color-coded CT angiography is a technically feasible technique that provides additional information on cerebral hemodynamics in ischemic strokepatients.
Authors: M Meijs; S A H Pegge; K Murayama; H D Boogaarts; M Prokop; P W A Willems; R Manniesing; F J A Meijer Journal: AJNR Am J Neuroradiol Date: 2019-08-08 Impact factor: 3.825