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Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke

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Abstract

Purpose

To determine the optimal slice thickness of brain non-contrast computed tomography using a hybrid iterative reconstruction algorithm to identify hyperdense middle cerebral artery sign in patients with acute ischemic stroke.

Methods

We retrospectively enrolled 30 patients who had presented hyperdense middle cerebral artery sign and 30 patients who showed no acute ischemic change in acute magnetic resonance imaging. Reformatted axial images at an angle of the orbitomeatal line in slice thicknesses of 0.5, 1, 3, 5, and 7 mm were generated. Optimal slice thickness for identifying hyperdense middle cerebral artery sign was evaluated by a receiver operating characteristics curve analysis and area under the curve (AUC).

Results

The mean AUC value of 0.5-mm slice (0.921; 95% confidence interval (95% CI), 0.868 to 0.975) was significantly higher than those of 3-mm (0.791; 95% CI, 0.686 to 0.895; p = 0.041), 5-mm (0.691; 95% CI, 0.583 to 0.799, p < 0.001), and 7-mm (0.695; 95% CI, 0.593 to 0.797, p < 0.001) slices, whereas it was equivalent to that of 1-mm slice (0.901; 95% CI, 0.837 to 0.965, p = 0.751).

Conclusion

Thin slice thickness of ≤ 1 mm has a better diagnostic performance for identifying hyperdense artery sign on brain non-contrast computed tomography with a hybrid iterative reconstruction algorithm in patients with acute ischemic stroke.

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References

  1. Gács G, Fox AJ, Barnett HJ, Vinuela F (1983) CT visualization of intracranial arterial thromboembolism. Stroke 14:756–762. https://doi.org/10.1161/01.STR.14.5.756

    Article  PubMed  Google Scholar 

  2. Pressman BD, Tourje EJ, Thompson JR (1987) An early CT sign of ischemic infarction: increased density in a cerebral artery. Am J Roentgenol 149:583–586. https://doi.org/10.2214/ajr.149.3.583

    Article  CAS  Google Scholar 

  3. Lombardi S, Riva L, Patassini M, Remida P, Capraro C, Canonico F, Franzesi CT, Ippolito D (2018) “Hyperdense artery sign” in early ischemic stroke: diagnostic value of model-based reconstruction approach in comparison with standard hybrid iterative reconstruction algorithm. Neuroradiology 60:1273–1280. https://doi.org/10.1007/s00234-018-2092-3

    Article  PubMed  Google Scholar 

  4. Jensen-Kondering U (2010) Hyperdense artery sign on computed tomography in acute ischemic stroke. World J Radiol 2:354–357. https://doi.org/10.4329/wjr.v2.i9.354

    Article  PubMed  PubMed Central  Google Scholar 

  5. Mair G, Boyd EV, Chappell FM, von Kummer R, Lindley RI, Sandercock P, Wardlaw JM, IST-3 Collaborative Group (2015) Sensitivity and specificity of the hyperdense artery sign for arterial obstruction in acute ischemic stroke. Stroke 46:102–107. https://doi.org/10.1161/STROKEAHA.114.007036

    Article  PubMed  Google Scholar 

  6. Eung YK, Lee SK, Dong JK et al (2005) Detection of thrombus in acute ischemic stroke: value of thin-section noncontrast-computed tomography. Stroke 36:2745–2747. https://doi.org/10.1161/01.STR.0000185720.03803.41

    Article  Google Scholar 

  7. Riedel CH, Jensen U, Rohr A, Tietke M, Alfke K, Ulmer S, Jansen O (2010) Assessment of thrombus in acute middle cerebral artery occlusion using thin-slice nonenhanced computed tomography reconstructions. Stroke 41:1659–1664. https://doi.org/10.1161/STROKEAHA.110.580662

    Article  PubMed  Google Scholar 

  8. Pontana F, Duhamel A, Pagniez J, Flohr T, Faivre JB, Hachulla AL, Remy J, Remy-Jardin M (2011) Chest computed tomography using iterative reconstruction vs filtered back projection (part 2): image quality of low-dose CT examinations in 80 patients. Eur Radiol 21:636–643. https://doi.org/10.1007/s00330-010-1991-4

    Article  PubMed  Google Scholar 

  9. Yamada Y, Jinzaki M, Hosokawa T, Tanami Y, Sugiura H, Abe T, Kuribayashi S (2012) Dose reduction in chest CT: comparison of the adaptive iterative dose reduction 3D, adaptive iterative dose reduction, and filtered back projection reconstruction techniques. Eur J Radiol 81:4185–4195. https://doi.org/10.1016/j.ejrad.2012.07.013

    Article  PubMed  Google Scholar 

  10. Chen CM, Lin YY, Hsu MY, Hung CF, Liao YL, Tsai HY (2016) Performance of adaptive iterative dose reduction 3D integrated with automatic tube current modulation in radiation dose and image noise reduction compared with filtered-back projection for 80-kVp abdominal CT: anthropomorphic phantom and patient study. Eur J Radiol 85:1666–1672. https://doi.org/10.1016/j.ejrad.2016.07.002

    Article  PubMed  Google Scholar 

  11. Nakaura T, Iyama Y, Kidoh M, Yokoyama K, Oda S, Tokuyasu S, Harada K, Yamashita Y (2016) Comparison of iterative model, hybrid iterative, and filtered back projection reconstruction techniques in low-dose brain CT: impact of thin-slice imaging. Neuroradiology 58:245–251. https://doi.org/10.1007/s00234-015-1631-4

    Article  PubMed  Google Scholar 

  12. Maamoun I, Khalil MM (2018) Assessment of iterative image reconstruction on kidney and liver donors: potential role of adaptive iterative dose reduction 3D (AIDR 3D) technology. Eur J Radiol 109:124–129. https://doi.org/10.1016/j.ejrad.2018.10.020

    Article  CAS  PubMed  Google Scholar 

  13. Hamamura T, Hayashida Y, Takeshita Y, Sugimoto K, Ueda I, Futatsuya K, Kakeda S, Aoki T, Korogi Y (2019) The usefulness of full-iterative reconstruction algorithm for the visualization of cystic artery on CT angiography. Jpn J Radiol 37:526–533. https://doi.org/10.1007/s11604-019-00839-x

    Article  PubMed  Google Scholar 

  14. Iyama Y, Nakaura T, Oda S, Kidoh M, Utsunomiya D, Yoshida M, Yuki H, Hirata K, Funama Y, Harada K, Awai K, Hirai T, Yamashita Y (2017) Iterative reconstruction designed for brain CT: a correlative study with filtered back projection for the diagnosis of acute ischemic stroke. J Comput Assist Tomogr 41:884–890. https://doi.org/10.1097/RCT.0000000000000626

    Article  PubMed  Google Scholar 

  15. Inoue T, Nakaura T, Yoshida M, Yokoyama K, Uetani H, Oda S, Utsunomiya D, Kitajima M, Harada K, Yamashita Y (2018) Brain computed tomography using iterative reconstruction to diagnose acute middle cerebral artery stroke: usefulness in combination of narrow window setting and thin slice reconstruction. Neuroradiology 60:373–379. https://doi.org/10.1007/s00234-018-1982-8

    Article  PubMed  Google Scholar 

  16. Liu X, Chen L, Qi W, Jiang Y, Liu Y, Zhang M, Hong N (2017) Thin-slice brain CT with iterative model reconstruction algorithm for small lacunar lesions detection: image quality and diagnostic accuracy evaluation. Medicine 96:e9412. https://doi.org/10.1097/MD.0000000000009412

    Article  PubMed  PubMed Central  Google Scholar 

  17. Smith BJ, Hillis SL, Pesce LL (2020) _MCMCaov: multi-reader multi-case analysis of variance_. R package version 0.1.10, <URL: https://github.com/brian-j-smith/MRMCaov>. Accessed 27 July 2020

  18. Smith BJ, Hillis SL (2020) Multi-reader multi-case analysis of variance software for diagnostic performance comparison of imaging modalities. In Samuelson F, Taylor-Phillips S (eds.), _Proceedings of SPIE 11316, Medical imaging 2020: image perception, observer performance, and technology assessment_, 113160K. doi: 10.117/12.2549075 (URL: 10.117/12.2549075),<URL:https://pubmed.ncbi.nlm.nih.gov/32351258>

  19. Chrzan R, Gleń A, Urbanik A (2017) Hyperdense middle cerebral artery sign as the only radiological manifestation of hyperacute ischemic stroke in computed tomography. Neurol Neurochir Pol 51:33–37. https://doi.org/10.1016/j.pjnns.2016.10.003

    Article  PubMed  Google Scholar 

  20. Riedel CH, Zoubie J, Ulmer S, Gierthmuehlen J, Jansen O (2012) Thin-slice reconstructions of nonenhanced CT images allow for detection of thrombus in acute stroke. Stroke 43:2319–2323. https://doi.org/10.1161/STROKEAHA.112.649921

    Article  PubMed  Google Scholar 

  21. Kanal KM, Stewart BK, Kolokythas O, Shuman WP (2007) Impact of operator-selected image noise index and reconstruction slice thickness on patient radiation dose in 64-MDCT. Am J Roentgenol 189:219–225. https://doi.org/10.2214/AJR.06.1524

    Article  Google Scholar 

  22. Ernst M, Romero JM, Buhk JH, Kemmling A, Fiehler J, Groth M (2014) Sensitivity of visual and quantitative detection of middle cerebral artery occlusion on non-contrast-enhanced computed tomography. Neuroradiology 56:1063–1068. https://doi.org/10.1007/s00234-014-1443-y

    Article  CAS  PubMed  Google Scholar 

  23. Krings T, Noelchen D, Mull M, Willmes K, Meister IG, Reinacher P, Toepper R, Thron AK (2006) The hyperdense posterior cerebral artery sign: a computed tomography marker of acute ischemia in the posterior cerebral artery territory. Stroke 37:399–403. https://doi.org/10.1161/01.STR.0000199062.09010.77

    Article  PubMed  Google Scholar 

  24. Goldmakher GV, Camargo ECS, Furie KL, Singhal AB, Roccatagliata L, Halpern EF, Chou MJ, Biagini T, Smith WS, Harris GJ, Dillon WP, Gonzalez RG, Koroshetz WJ, Lev MH (2009) Hyperdense basilar artery sign on unenhanced CT predicts thrombus and outcome in acute posterior circulation stroke. Stroke 40:134–139. https://doi.org/10.1161/STROKEAHA.108.516690

    Article  PubMed  Google Scholar 

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Acknowledgments

We thank Shun Hayasaka (Sapporo Medical University Hospital) for assistance with statistical analysis and Miho Kobayashi (Kurashiki Central Hospital) for her extensive proofreading.

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Authors and Affiliations

  1. Department of Radiological Technology, Kurashiki Central Hospital, 1-1-1 Miwa, Kurashiki, Okayama, 710-8602, Japan

    Shota Ichikawa, Misaki Hamada, Daiki Watanabe, Osamu Ito, Takafumi Moriya & Hiroyuki Yamamoto

Authors
  1. Shota Ichikawa
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  2. Misaki Hamada
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  3. Daiki Watanabe
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  4. Osamu Ito
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  5. Takafumi Moriya
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  6. Hiroyuki Yamamoto
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Shota Ichikawa, Misaki Hamada, Daiki Watanabe, Osamu Ito, Takafumi Moriya, and Hiroyuki Yamamoto. The first draft of the manuscript was written by Shota Ichikawa, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shota Ichikawa.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the hospital ethics committee.

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For this type of study, requirement for individual informed consent was waived by the local ethics committee.

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Ichikawa, S., Hamada, M., Watanabe, D. et al. Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke. Emerg Radiol 28, 309–315 (2021). https://doi.org/10.1007/s10140-020-01864-4

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  • DOI: https://doi.org/10.1007/s10140-020-01864-4

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