Dual-Layer Detector Cone-Beam CT Angiography for Stroke Assessment: First-in-Human Results (the Next Generation X-ray Imaging System Trial)

References

1. 1.↵
   1. Jaffray DA,
   2. Siewerdsen JH

   . Cone-beam computed tomography with a flat-panel imager: initial performance characterization. Med Phys 2000;27:1311–23 doi:10.1118/1.599009 pmid:10902561

   CrossRefPubMed
2. 2.↵
   1. Soderman M,
   2. Babic D,
   3. Holmin S, et al

   . Brain imaging with a flat detector C-arm: Technique and clinical interest of XperCT. Neuroradiology 2008;50:863–68 doi:10.1007/s00234-008-0419-1 pmid:18560818

   CrossRefPubMed
3. 3.↵
   1. Dijkstra ML,
   2. Eagleton MJ,
   3. Greenberg RK, et al

   . Intraoperative C-arm cone-beam computed tomography in fenestrated/branched aortic endografting. J Vasc Surg 2011;53:583–90 doi:10.1016/j.jvs.2010.09.039 pmid:21129898

   CrossRefPubMed
4. 4.↵
   1. Tacher V,
   2. Radaelli A,
   3. Lin M, et al

   . How I do it: cone-beam CT during transarterial chemoembolization for liver cancer. Radiology 2015;274:320–34 doi:10.1148/radiol.14131925 pmid:25625741

   CrossRefPubMed
5. 5.↵
   1. Doerfler A,
   2. Golitz P,
   3. Engelhorn T, et al

   . Flat-panel computed tomography (DYNA-CT) in neuroradiology: from high-resolution imaging of implants to one-stop-shopping for acute stroke. Clin Neuroradiol 2015;25 Suppl 2:291–97 doi:10.1007/s00062-015-0423-x pmid:26091842

   CrossRefPubMed
6. 6.↵
   1. Brehm A,
   2. Tsogkas I,
   3. Maier IL, et al

   . One-stop management with perfusion for transfer patients with stroke due to a large-vessel occlusion: feasibility and effects on in-hospital times. AJNR J Neuroradiol 2019;40:1330–34 doi:10.3174/ajnr.A6129 pmid:31296523

   CrossRefPubMed
7. 7.↵
   1. Struffert T,
   2. Deuerling-Zheng Y,
   3. Kloska S, et al

   . Flat detector CT in the evaluation of brain parenchyma, intracranial vasculature, and cerebral blood volume: a pilot study in patients with acute symptoms of cerebral ischemia. AJNR Am J Neuroradiol 2010;31:1462–69 doi:10.3174/ajnr.A2083 pmid:20378700

   CrossRefPubMed
8. 8.↵
   1. Struffert T,
   2. Deuerling-Zheng Y,
   3. Kloska S, et al

   . Dynamic angiography and perfusion imaging using flat detector CT in the angiography suite: a pilot study in patients with acute middle cerebral artery occlusions. AJNR Am J Neuroradiol 2015;36:1964–70 doi:10.3174/ajnr.A4383 pmid:26066625

   CrossRefPubMed
9. 9.↵
   1. Saake M,
   2. Breuer L,
   3. Goelitz P, et al

   . Flat detector computed tomography angiography with intravenous contrast application: feasibility for visualization of cerebral arterial vasculature. J Neuroimaging 2013;23:414–20 doi:10.1111/j.1552-6569.2012.00762.x pmid:23279381

   CrossRefPubMed
10. 10.↵
    1. Hoelter P,
    2. Goelitz P,
    3. Lang S, et al

    . Visualization of large vessel occlusion, clot extent, and collateral supply using volume perfusion flat detector computed tomography in acute patients with stroke. Acta Radiol 2019;60:1504–11 doi:10.1177/0284185119836220 pmid:30862169

    CrossRefPubMed
11. 11.↵
    1. Schneider D,
    2. Apfaltrer P,
    3. Sudarski S, et al

    . Optimization of kiloelectron volt settings in cerebral and cervical dual-energy CT angiography determined with virtual monoenergetic imaging. Acad Radiol 2014;21:431–36 doi:10.1016/j.acra.2013.12.006 pmid:24594412

    CrossRefPubMed
12. 12.↵
    1. Leithner D,
    2. Mahmoudi S,
    3. Wichmann JL, et al

    . Evaluation of virtual monoenergetic imaging algorithms for dual-energy carotid and intracerebral CT angiography: effects on image quality, artefacts and diagnostic performance for the detection of stenosis. Eur J Radiol 2018;99:111–17 doi:10.1016/j.ejrad.2017.12.024 pmid:29362140

    CrossRefPubMed
13. 13.↵
    1. Neuhaus V,
    2. Große Hokamp N,
    3. Abdullayev N, et al

    . Comparison of virtual monoenergetic and polyenergetic images reconstructed from dual-layer detector CT angiography of the head and neck. Eur Radiol 2018;28:1102–10 doi:10.1007/s00330-017-5081-8 pmid:29018958

    CrossRefPubMed
14. 14.↵
    1. Fredenberg E

    . Spectral and dual-energy X-ray imaging for medical applications. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2018;878:74–87 doi:10.1016/j.nima.2017.07.044

    CrossRef
15. 15.↵
    1. Ståhl F,
    2. Schäfer D,
    3. Omar A, et al

    . Performance characterization of a prototype dual-layer cone-beam computed tomography system. Med Phys 2021;48:6740–54 doi:10.1002/mp.15240 pmid:34622973

    CrossRefPubMed
16. 16.↵
    1. Keselman B,
    2. Berglund A,
    3. Ahmed N, et al

    . The Stockholm Stroke Triage Project: outcomes of endovascular thrombectomy before and after triage implementation. Stroke 2022;53:473–81 doi:10.1161/STROKEAHA.121.034195 pmid:34583527

    CrossRefPubMed
17. 17.↵
    1. Brown KM,
    2. Zabic S,
    3. Shechter G

    . Impact of spectral separation in dual-energy CT with anti-correlated statistical reconstruction. Proceedings of the Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Worcester, Massachusetts. July 2, 2015:491–94
18. 18.↵
    1. Zopfs D,
    2. Lennartz S,
    3. Abdullayev N, et al

    . Generally applicable window settings of low-keV virtual monoenergetic reconstructions in dual-layer CT-angiography of the head and neck. Quant Imaging Med Surg 2021;11:3408–17 doi:10.21037/qims-20-1140 pmid:34341719

    CrossRefPubMed
19. 19.↵
    1. Hinkmann FM,
    2. Voit HL,
    3. Anders K, et al

    . Ultra-fast carotid CT-angiography: low versus standard volume contrast material protocol for a 128-slice CT-system. Invest Radiol 2009;44:257–64 doi:10.1097/RLI.0b013e31819b08a0 pmid:19550377

    CrossRefPubMed
20. 20.↵
    1. Mantel N,
    2. Haenszel W

    . Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 1959;22:719–48 pmid:13655060

    PubMed
21. 21.↵
    1. Fleiss JL

    . Measuring nominal scale agreement among many raters. Psychol Bull 1971;76:378–82 doi:10.1037/h0031619

    CrossRef
22. 22.↵
    1. Cancelliere NM,
    2. Hummel E,
    3. van Nijnatten F, et al

    . The butterfly effect: improving brain cone-beam CT image artifacts for stroke assessment using a novel dual-axis trajectory. J Neurointerv Surg 2023;15;283–87 doi:10.1136/neurintsurg-2021-018553 pmid:35478176

    CrossRefPubMed
23. 23.↵
    1. Leyhe JR,
    2. Tsogkas I,
    3. Hesse AC, et al

    . Latest generation of flat detector CT as a peri-interventional diagnostic tool: a comparative study with multidetector CT. J Neurointerv Surg 2017;9:1253–57 doi:10.1136/neurintsurg-2016-012866 pmid:27998955

    CrossRefPubMed
24. 24.↵
    1. Ouadah S,
    2. Jacobson M,
    3. Stayman JW, et al

    . Correction of patient motion in cone-beam CT using 3D–2D registration. Phys Med Biol 2017;62:8813–31 doi:10.1088/1361-6560/aa9254 pmid:28994668

    CrossRefPubMed
25. 25.↵
    1. Li Y,
    2. Garrett JW,
    3. Li K, et al

    . Time-resolved C-arm cone beam CT angiography (TR-CBCTA) imaging from a single short-scan C-arm cone beam CT acquisition with intra-arterial contrast injection. Phys Med Biol 2018;63:075001 doi:10.1088/1361-6560/aab346 pmid:29494345

    CrossRefPubMed
26. 26.
    American Association of Physicists in Medicine. Report of American Association of Physicists in Medicine Task Group 111: The Future of CT Dosimetry: Comprehensive Methodology for the Evaluation of Radiation Dose in X-Ray Computed Tomography (2010). https://www.aapm.org/pubs/reports/detail.asp?docid=109. Accessed September 6, 2022