Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Lang S

PROVIDER: S-EPMC8562747 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Lang S S Hoelter P P Schmidt M M Strother C C Kaethner C C Kowarschik M M Doerfler A A

AJNR. American journal of neuroradiology 20210909 10

<h4>Background and purpose</h4>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.<h4>Materials and methods</h4>3D-DSA data sets of cerebral aneurysms ...[more]

PMID: 34503946

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Project description:BackgroundAn accurate quantitative analysis of coronary artery stenotic lesions is essential to make optimal clinical decisions. Recent advances in computer vision and machine learning technology have enabled the automated analysis of coronary angiography.ObjectiveThe aim of this paper is to validate the performance of artificial intelligence-based quantitative coronary angiography (AI-QCA) in comparison with that of intravascular ultrasound (IVUS).MethodsThis retrospective study included patients who underwent IVUS-guided coronary intervention at a single tertiary center in Korea. Proximal and distal reference areas, minimal luminal area, percent plaque burden, and lesion length were measured by AI-QCA and human experts using IVUS. First, fully automated QCA analysis was compared with IVUS analysis. Next, we adjusted the proximal and distal margins of AI-QCA to avoid geographic mismatch. Scatter plots, Pearson correlation coefficients, and Bland-Altman were used to analyze the data.ResultsA total of 54 significant lesions were analyzed in 47 patients. The proximal and distal reference areas, as well as the minimal luminal area, showed moderate to strong correlation between the 2 modalities (correlation coefficients of 0.57, 0.80, and 0.52, respectively; P<.001). The correlation was weaker for percent area stenosis and lesion length, although statistically significant (correlation coefficients of 0.29 and 0.33, respectively). AI-QCA tended to measure reference vessel areas smaller and lesion lengths shorter than IVUS did. Systemic proportional bias was not observed in Bland-Altman plots. The biggest cause of bias originated from the geographic mismatch of AI-QCA with IVUS. Discrepancies in the proximal or distal lesion margins were observed between the 2 modalities, which were more frequent at the distal margins. After the adjustment of proximal or distal margins, there was a stronger correlation of proximal and distal reference areas between AI-QCA and IVUS (correlation coefficients of 0.70 and 0.83, respectively).ConclusionsAI-QCA showed a moderate to strong correlation compared with IVUS in analyzing coronary lesions with significant stenosis. The main discrepancy was in the perception of the distal margins by AI-QCA, and the correction of margins improved the correlation coefficients. We believe that this novel tool could provide confidence to treating physicians and help in making optimal clinical decisions.

Dataset Information

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

Background and purpose

Materials and methods

Results

Conclusions

Publications

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

Similar Datasets

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