Artificial Intelligence-Based 3D Angiography for Visualization of Complex Cerebrovascular Pathologies
© 2021 by American Journal of Neuroradiology..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:42 |
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| Enthalten in: |
AJNR. American journal of neuroradiology - 42(2021), 10 vom: 01. Okt., Seite 1762-1768 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lang, S [VerfasserIn] |
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| Anmerkungen: |
Date Completed 24.11.2021 Date Revised 03.10.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.3174/ajnr.A7252 |
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| Förderinstitution / Projekttitel: |
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| 500 | |a Date Revised 03.10.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2021 by American Journal of Neuroradiology. | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Kaethner, C |e verfasserin |4 aut | |
| 700 | 1 | |a Kowarschik, M |e verfasserin |4 aut | |
| 700 | 1 | |a Doerfler, A |e verfasserin |4 aut | |
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