European Radiology

, Volume 28, Issue 2, pp 770–779 | Cite as

Could new reconstruction CT techniques challenge MRI for the detection of brain metastases in the context of initial lung cancer staging?

  • Domitille Millon
  • David Byl
  • Philippe Collard
  • Samantha E. Cambier
  • Aline G. Van Maanen
  • Alain Vlassenbroek
  • Emmanuel E. Coche
Computed Tomography
First Online:
Received:
Revised:
Accepted:
  • 297 Downloads

Abstract

Objectives

To evaluate the diagnostic performance of brain CT images reconstructed with a model-based iterative algorithm performed at usual and reduced dose.

Methods

115 patients with histologically proven lung cancer were prospectively included over 15 months. Patients underwent two CT acquisitions at the initial staging, performed on a 256-slice MDCT, at standard (CTDIvol: 41.4 mGy) and half dose (CTDIvol: 20.7 mGy). Both image datasets were reconstructed with filtered back projection (FBP) and iterative model-based reconstruction (IMR) algorithms. Brain MRI was considered as the reference. Two blinded independent readers analysed the images.

Results

Ninety-three patients underwent all examinations. At the standard dose, eight patients presented 17 and 15 lesions on IMR and FBP CT images, respectively. At half-dose, seven patients presented 15 and 13 lesions on IMR and FBP CT images, respectively. The test could not highlight any significant difference between the standard dose IMR and the half-dose FBP techniques (p-value = 0.12). MRI showed 46 metastases on 11 patients. Specificity, negative and positive predictive values were calculated (98.9–100 %, 93.6–94.6 %, 75–100 %, respectively, for all CT techniques).

Conclusion

No significant difference could be demonstrated between the two CT reconstruction techniques.

Key points

• No significant difference between IMR100 and FBP50 was shown.

• Compared to FBP, IMR increased the image quality without diagnostic impairment.

• A 50 % dose reduction combined with IMR reconstructions could be achieved.

• Brain MRI remains the best tool in lung cancer staging.

Keywords

Brain CT with dose reduction Model-Based Iterative Reconstruction Diagnostic performance Brain metastases Lung cancer staging 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

Our work was presented at ECR 2017, in Vienna, during the SS1011a session (Brain tumours: imaging techniques).

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Pr. Emmanuel COCHE.

Conflict of interest

One author (Alain Vassenbroek) of this manuscript declares relationships with the following companies: Philips Healthcare.

Statistics and biometry

Two of the authors have significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• prospective

• experimental

• performed at one institution

References

  1. 1.
    Brenner DJ, Hall EJ (2007) Computed tomography--an increasing source of radiation exposure. N Engl J Med 357:2277–2284CrossRefPubMedGoogle Scholar
  2. 2.
    Geyer LL, Schoepf UJ, Meinel FG et al (2015) State of the Art: Iterative CT Reconstruction Techniques. Radiology 276:339–357CrossRefPubMedGoogle Scholar
  3. 3.
    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
  4. 4.
    Murphy KP, Crush L, O'Neill SB et al (2016) Feasibility of low-dose CT with model-based iterative image reconstruction in follow-up of patients with testicular cancer. Eur J Radiol Open 3:38–45CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Gandhi NS, Baker ME, Goenka AH et al (2016) Diagnostic Accuracy of CT Enterography for Active Inflammatory Terminal Ileal Crohn Disease: Comparison of Full-Dose and Half-Dose Images Reconstructed with FBP and Half-Dose Images with SAFIRE. Radiology 280:436–445CrossRefPubMedGoogle Scholar
  6. 6.
    Wu TH, Hung SC, Sun JY et al (2013) How far can the radiation dose be lowered in head CT with iterative reconstruction? Analysis of imaging quality and diagnostic accuracy. Eur Radiol 23:2612–2621CrossRefPubMedGoogle Scholar
  7. 7.
    Millon D, Vlassenbroek A, Van Maanen AG, Cambier SE, Coche EE (2016) Low contrast detectability and spatial resolution with model-based Iterative reconstructions of MDCT images: a phantom and cadaveric study. Eur Radiol. doi: https://doi.org/10.1007/s00330-016-4444-x
  8. 8.
    Love A, Olsson ML, Siemund R, Stalhammar F, Bjorkman-Burtscher IM, Soderberg M (2013) Six iterative reconstruction algorithms in brain CT: a phantom study on image quality at different radiation dose levels. Br J Radiol 86:20130388CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Campos S, Davey P, Hird A et al (2009) Brain metastasis from an unknown primary, or primary brain tumour? A diagnostic dilemma. Curr Oncol 16:62–66PubMedPubMedCentralGoogle Scholar
  10. 10.
    Landis SH, Murray T, Bolden S, Wingo PA (1998) Cancer statistics, 1998. CA Cancer J Clin 48:6–29CrossRefPubMedGoogle Scholar
  11. 11.
    Nussbaum ES, Djalilian HR, Cho KH, Hall WA (1996) Brain metastases. Histology, multiplicity, surgery, and survival. Cancer 78:1781–1788CrossRefPubMedGoogle Scholar
  12. 12.
    Schellinger PD, Meinck HM, Thron A (1999) Diagnostic accuracy of MRI compared to CCT in patients with brain metastases. J Neurooncol 44:275–281CrossRefPubMedGoogle Scholar
  13. 13.
    Park HY, Kim YH, Kim H et al (2007) Routine screening by brain magnetic resonance imaging decreased the brain metastasis rate following surgery for lung adenocarcinoma. Lung Cancer 58:68–72CrossRefPubMedGoogle Scholar
  14. 14.
    McCollough CH (2010) Diagnostic Reference Levels. American College of RadiologyGoogle Scholar
  15. 15.
    Bongartz G, Golging SJ, Jurik AG, Leonardi M, Van Meerten EVP (1999) European guidelines on quality criteria for computed tomography. European Commission, LuxembourgGoogle Scholar
  16. 16.
    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
  17. 17.
    Notohamiprodjo S, Deak Z, Meurer F et al (2015) Image quality of iterative reconstruction in cranial CT imaging: comparison of model-based iterative reconstruction (MBIR) and adaptive statistical iterative reconstruction (ASiR). Eur Radiol 25:140–146CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2017

Authors and Affiliations

  1. 1.Department of Radiology and Medical Imaging, Cliniques Universitaires Saint LucUniversité Catholique de LouvainBrusselsBelgium
  2. 2.Department of Pneumology, Cliniques Universitaires Saint LucUniversité Catholique de LouvainBrusselsBelgium
  3. 3.Statistic Unit, King Albert II Cancer InstituteUniversité Catholique de LouvainBrusselsBelgium
  4. 4.Philips HealthcareBrusselsBelgium

Personalised recommendations