European Radiology

, Volume 27, Issue 9, pp 3710–3715 | Cite as

Diagnosis of small posterior fossa stroke on brain CT: effect of iterative reconstruction designed for brain CT on detection performance

  • Taihei Inoue
  • Takeshi Nakaura
  • Morikatsu Yoshida
  • Koichi Yokoyama
  • Kenichiro Hirata
  • Masafumi Kidoh
  • Seitaro Oda
  • Daisuke Utsunomiya
  • Kazunori Harada
  • Yasuyuki Yamashita



In this study, we aimed to determine whether iterative model reconstruction designed for brain CT (IMR-neuro) would improve the accuracy of posterior fossa stroke diagnosis on brain CT.


We enrolled 37 patients with ischaemic stroke in the posterior fossa and 37 patients without stroke (controls). Using axial images reconstructed using filtered back-projection (FBP) and IMR-neuro, we compared the CT numbers in infarcted areas, image noise in the pons, and contrast-to-noise ratios (CNRs) of infarcted and non-infarcted areas on scans subjected to IMR-neuro and FBP. To analyse the performance of hypo-attenuation detection, we used receiver-operating characteristic (ROC) curve techniques.


The image noise was significantly lower (2.2 ± 0.5 vs. 5.1 ± 0.9 Hounsfield units, p < 0.01) and the difference in CNR between the infarcted and non-infarcted areas was significantly higher with IMR-neuro than with FBP (2.2 ± 1.7 vs. 4.0 ± 3.6, p < 0.01). Furthermore, the average area under the ROC curve was significantly higher with IMR-neuro (0.90 vs. 0.86 for FBP, p = 0.04).


IMR-neuro yielded better image quality and improved hypo-attenuation detection in patients with ischaemic stroke.

Key points

Iterative model reconstruction of brain CT data can facilitate the diagnosis of ischaemic stroke.

IMR improved the detectability of low-contrast lesions in the posterior fossa.

IMR-neuro yielded better image quality and improved observer performance.


Computed tomography Image processing Stroke Radiation dosage Posterior fossa 



The scientific guarantor of this publication is Yasuyuki Yamashita. The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding. No complex statistical methods were necessary for this paper. Written informed consent was waived by the Institutional Review Board. Institutional Review Board approval was obtained. Methodology: retrospective, diagnostic or prognostic study, performed at one institution.


  1. 1.
    Bernick C, Kuller L, Dulberg C et al (2001) Silent MRI infarcts and the risk of future stroke: the cardiovascular health study. Neurology 57:1222–1229CrossRefPubMedGoogle Scholar
  2. 2.
    Joseph PM, Ruth C (1997) A method for simultaneous correction of spectrum hardening artifacts in CT images containing both bone and iodine. Med Phys 24:1629–1634CrossRefPubMedGoogle Scholar
  3. 3.
    Rozeik C, Kotterer O, Preiss J, Schutz M, Dingler W, Deininger HK (1991) Cranial CT artifacts and gantry angulation. J Comput Assist Tomogr 15:381–386CrossRefPubMedGoogle Scholar
  4. 4.
    Ogawa A, Mori E, Minematsu K et al (2007) Randomized trial of intraarterial infusion of urokinase within 6 hours of middle cerebral artery stroke: the middle cerebral artery embolism local fibrinolytic intervention trial (MELT) Japan. Stroke 38:2633–2639CrossRefPubMedGoogle Scholar
  5. 5.
    Block KT, Uecker M, Frahm J (2009) Model-based iterative reconstruction for radial fast spin-echo MRI. IEEE Trans Med Imaging 28:1759–1769CrossRefPubMedGoogle Scholar
  6. 6.
    Deak Z, Grimm JM, Treitl M et al (2013) Filtered back projection, adaptive statistical iterative reconstruction, and a model-based iterative reconstruction in abdominal CT: an experimental clinical study. Radiology 266:197–206CrossRefPubMedGoogle Scholar
  7. 7.
    Pickhardt PJ, Lubner MG, Kim DH et al (2012) Abdominal CT with model-based iterative reconstruction (MBIR): initial results of a prospective trial comparing ultralow-dose with standard-dose imaging. AJR Am J Roentgenol 199:1266–1274CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Katsura M, Matsuda I, Akahane M et al (2012) Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique. Eur Radiol 22:1613–1623CrossRefPubMedGoogle Scholar
  9. 9.
    Chang W, Lee JM, Lee K et al (2013) Assessment of a model-based, iterative reconstruction algorithm (MBIR) regarding image quality and dose reduction in liver computed tomography. Invest Radiol 48:598–606CrossRefPubMedGoogle Scholar
  10. 10.
    Nakaura T, Iyama Y, Kidoh M et al (2016) Comparison of iterative model, hybrid iterative, and filtered back projection reconstruction techniques in low-dose brain CT: impact of thin-slice imaging. Neuroradiology 58:245–251CrossRefPubMedGoogle Scholar
  11. 11.
    Puetz V, Sylaja PN, Coutts SB et al (2008) Extent of hypoattenuation on CT angiography source images predicts functional outcome in patients with basilar artery occlusion. Stroke 39:2485–2490CrossRefPubMedGoogle Scholar
  12. 12.
    Koc G, Courtier JL, Phelps A, Marcovici PA, MacKenzie JD (2014) Computed tomography depiction of small pediatric vessels with model-based iterative reconstruction. Pediatr Radiol 44:787–794CrossRefPubMedGoogle Scholar
  13. 13.
    Dzialowski I, Weber J, Doerfler A, Forsting M, von Kummer R (2004) Brain tissue water uptake after middle cerebral artery occlusion assessed with CT. J Neuroimaging 14:42–48CrossRefPubMedGoogle Scholar
  14. 14.
    Brooks RA, Di Chiro G (1976) Statistical limitations in x-ray reconstructive tomography. Med Phys 3:237–240CrossRefPubMedGoogle Scholar
  15. 15.
    Barber PA, Demchuk AM, Zhang J, Buchan AM (2000) Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. ASPECTS Study Group. Alberta Stroke Programme Early CT Score Lancet 355:1670–1674CrossRefPubMedGoogle Scholar
  16. 16.
    Haubenreisser H, Fink C, Nance JW Jr et al (2014) Feasibility of slice width reduction for spiral cranial computed tomography using iterative image reconstruction. Eur J Radiol 83:964–969CrossRefPubMedGoogle Scholar
  17. 17.
    Kijewski PK, Bjarngard BE (1978) Correction for beam hardening in computed tomography. Med Phys 5:209–214CrossRefPubMedGoogle Scholar
  18. 18.
    Kidoh M, Nakaura T, Awai K et al (2013) Novel connecting tube for saline chaser in contrast-enhanced CT: the effect of spiral flow of saline on contrast enhancement. Eur Radiol 23:3213–3218CrossRefPubMedGoogle Scholar
  19. 19.
    Nakaura T, Kidoh M, Sakaino N et al (2013) Low radiation dose protocol in cardiac CT with 100 kVp: usefulness of display preset optimization. Int J Cardiovasc Imaging 29:1381–1389CrossRefPubMedGoogle Scholar
  20. 20.
    Kidoh M, Nakaura T, Nakamura S et al (2014) Reduction of dental metallic artifacts in CT: value of a newly developed algorithm for metal artifact reduction (O-MAR). Clin Radiol 69:e11–e16CrossRefPubMedGoogle Scholar
  21. 21.
    Kidoh M, Nakaura T, Nakamura S et al (2014) Low-contrast-dose protocol in cardiac CT: 20% contrast dose reduction using 100 kVp and high-tube-current-time setting in 256-slice CT. Acta Radiol 55:545–553CrossRefPubMedGoogle Scholar
  22. 22.
    Nakaura T, Nagayoshi Y, Awai K et al (2014) Myocardial bridging is associated with coronary atherosclerosis in the segment proximal to the site of bridging. J Cardiol 63:134–139CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2017

Authors and Affiliations

  • Taihei Inoue
    • 1
  • Takeshi Nakaura
    • 2
  • Morikatsu Yoshida
    • 1
  • Koichi Yokoyama
    • 1
  • Kenichiro Hirata
    • 2
  • Masafumi Kidoh
    • 2
  • Seitaro Oda
    • 2
  • Daisuke Utsunomiya
    • 2
  • Kazunori Harada
    • 3
  • Yasuyuki Yamashita
    • 2
  1. 1.Department of RadiologyAmakusa Medical CenterAmakusaJapan
  2. 2.Department of Diagnostic Radiology, Graduate School of Life SciencesKumamoto UniversityKumamotoJapan
  3. 3.Department of SurgeryAmakusa Medical CenterKumamotoJapan

Personalised recommendations