Artificial Intelligence-Based 3D Angiography for Visualization of Complex Cerebrovascular Pathologies.

BACKGROUND AND PURPOSE: By means of artificial intelligence, 3D angiography is a novel postprocessing method for 3D imaging of cerebral vessels. Because 3D angiography does not require a mask run like the current standard 3D-DSA, it potentially offers a considerable reduction of the patient radiation dose. Our aim was an assessment of the diagnostic value of 3D angiography for visualization of cerebrovascular pathologies. MATERIALS AND METHODS: 3D-DSA data sets of cerebral aneurysms (n CA = 10), AVMs (n AVM = 10), and dural arteriovenous fistulas (dAVFs) (n dAVF = 10) were reconstructed using both conventional and prototype software. Corresponding reconstructions have been analyzed by 2 neuroradiologists in a consensus reading in terms of image quality, injection vessel diameters (vessel diameter [VD] 1/2), vessel geometry index (VGI = VD1/VD2), and specific qualitative/quantitative parameters of AVMs (eg, location, nidus size, feeder, associated aneurysms, drainage, Spetzler-Martin score), dAVFs (eg, fistulous point, main feeder, diameter of the main feeder, drainage), and cerebral aneurysms (location, neck, size). RESULTS: In total, 60 volumes have been successfully reconstructed with equivalent image quality. The specific qualitative/quantitative assessment of 3D angiography revealed nearly complete accordance with 3D-DSA in AVMs (eg, mean nidus size3D angiography/3D-DSA= 19.9 [SD, 10.9]/20.2 [SD, 11.2] mm; r = 0.9, P = .001), dAVFs (eg, mean diameter of the main feeder3D angiography/3D-DSA= 2.04 [SD, 0.65]/2.05 [SD, 0.63] mm; r = 0.9, P = .001), and cerebral aneurysms (eg, mean size3D angiography/3D-DSA= 5.17 [SD, 3.4]/5.12 [SD, 3.3] mm; r = 0.9, P = .001). Assessment of the geometry of the injection vessel in 3D angiography data sets did not differ significantly from that of 3D-DSA (vessel geometry indexAVM: r = 0.84, P = .003; vessel geometry indexdAVF: r = 0.82, P = .003; vessel geometry indexCA: r = 0.84, P <.001). CONCLUSIONS: In this study, the artificial intelligence-based 3D angiography was a reliable method for visualization of complex cerebrovascular pathologies and showed results comparable with those of 3D-DSA. Thus, 3D angiography is a promising postprocessing method that provides a significant reduction of the patient radiation dose.

Quantitative and Qualitative Comparison of 4D-DSA with 3D-DSA Using Computational Fluid Dynamics Simulations in Cerebral Aneurysms.

BACKGROUND AND PURPOSE: 4D-DSA allows time-resolved 3D imaging of the cerebral vasculature. The aim of our study was to evaluate this method in comparison with the current criterion standard 3D-DSA by qualitative and quantitative means using computational fluid dynamics. MATERIALS AND METHODS: 3D- and 4D-DSA datasets were acquired in patients with cerebral aneurysms. Computational fluid dynamics analysis was performed for all datasets. Using computational fluid dynamics, we compared 4D-DSA with 3D-DSA in terms of both aneurysmal geometry (quantitative: maximum diameter, ostium size [OZ1/2], volume) and hemodynamic parameters (qualitative: flow stability, flow complexity, inflow concentration; quantitative: average/maximum wall shear stress, impingement zone, low-stress zone, intra-aneurysmal pressure, and flow velocity). Qualitative parameters were descriptively analyzed. Correlation coefficients (r, P value) were calculated for quantitative parameters. RESULTS: 3D- and 4D-DSA datasets of 10 cerebral aneurysms in 10 patients were postprocessed. Evaluation of aneurysmal geometry with 4D-DSA (r maximum diameter = 0.98, P maximum diameter <.001; r OZ1/OZ2 = 0.98/0.86, P OZ1/OZ2 < .001/.002; r volume = 0.98, P volume <.001) correlated highly with 3D-DSA. Evaluation of qualitative hemodynamic parameters (flow stability, flow complexity, inflow concentration) did show complete accordance, and evaluation of quantitative hemodynamic parameters (r average/maximum wall shear stress diastole = 0.92/0.88, P average/maximum wall shear stress diastole < .001/.001; r average/maximum wall shear stress systole = 0.94/0.93, P average/maximum wall shear stress systole < .001/.001; r impingement zone = 0.96, P impingement zone < .001; r low-stress zone = 1.00, P low-stress zone = .01; r pressure diastole = 0.84, P pressure diastole = .002; r pressure systole = 0.9, P pressure systole < .001; r flow velocity diastole = 0.95, P flow velocity diastole < .001; r flow velocity systole = 0.93, P flow velocity systole < .001) did show nearly complete accordance between 4D- and 3D-DSA. CONCLUSIONS: Despite a different injection protocol, 4D-DSA is a reliable basis for computational fluid dynamics analysis of the intracranial vasculature and provides equivalent visualization of aneurysm geometry compared with 3D-DSA.