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Evaluation of an Artificial Intelligence-Based 3D-Angiography for Visualization of Cerebral Vasculature

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Abstract

Purpose

The three-dimensional digital subtraction angiography (3D DSA) technique is the current standard and is based on both mask and fill runs to enable the subtraction technique. Artificial intelligence (AI)-based 3D angiography (3DA) was developed to reduce radiation dosage because only one contrast-enhanced run of the C‑arm system is required for reconstruction of DSA-like 3D volumes. The aim was the evaluation of this algorithm regarding its diagnostic information.

Methods

3D DSA datasets without pathologic findings were reconstructed both with subtraction technique and with the AI-based algorithm. Corresponding reconstructions were evaluated by 2 neuroradiologists with respect to image quality (IQ), visualization of major segments of the circle of Willis (ICA = C4-C7; OphA; ACA = A1-A2, MCA = M1-M2; VA = V4; BA; AICA; SUCA; PCA = P1-P2), identifiability of perforators (lenticulostriate/thalamoperforating arteries) and vessel diameters (ICA = C4; MCA = M1; BA; PCA = P1).

Results

In total 15 datasets were successfully reconstructed as 3D DSA and 3DA with diagnostic image quality. All major segments of the circle of Willis and perforators were comparably visualized with 3DA. Quantitative analysis of vessel diameters in 3D DSA and 3DA datasets was equivalent and did not show relevant differences (rICA = 0.901, p = 0.001; rM1 = 0.951, p = 0.001; rBA = 0.906, p = 0.001; rP1 = 0.991, p = 0.001).

Conclusions

The use of 3DA demonstrated reliable visualization of cerebral vasculature with respect to quantitative and qualitative parameters. Therefore, 3DA is a promising method that might help to reduce patient radiation.

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Authors and Affiliations

  1. Department of Neuroradiology, University of Erlangen-Nuremberg, Schwabachanlage 6, 91054, Erlangen, Germany

    Stefan Lang, Philip Hoelter, Manuel Schmidt, Felix Eisenhut, Hannes Lücking & Arnd Doerfler

  2. Angiography & Interventional X‑Ray Systems, Siemens Healthcare GmbH, Siemensstraße 1, 91301, Forchheim, Germany

    Christian Kaethner & Markus Kowarschik

Authors
  1. Stefan Lang
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  2. Philip Hoelter
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  3. Manuel Schmidt
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  4. Felix Eisenhut
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  5. Christian Kaethner
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  6. Markus Kowarschik
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  7. Hannes Lücking
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  8. Arnd Doerfler
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Corresponding author

Correspondence to Stefan Lang.

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Conflict of interest

S. Lang, P. Hoelter, S. Manuel, F. Eisenhut, C. Kaethner, M. Kowarschik, H. Lücking and A. Doerfler declare that they have no competing interests.

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The concepts and results presented in this paper are based on research and are not commercially available.

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Lang, S., Hoelter, P., Schmidt, M. et al. Evaluation of an Artificial Intelligence-Based 3D-Angiography for Visualization of Cerebral Vasculature. Clin Neuroradiol 30, 705–712 (2020). https://doi.org/10.1007/s00062-019-00836-7

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  • DOI: https://doi.org/10.1007/s00062-019-00836-7

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