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 size_{3D 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 feeder_{3D angiography/3D-DSA}= 2.04 [SD, 0.65]/2.05 [SD, 0.63] mm; r = 0.9, P = .001), and cerebral aneurysms (eg, mean size_{3D 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 index_AVM: r = 0.84, P = .003; vessel geometry index_dAVF: r = 0.82, P = .003; vessel geometry index_CA: 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