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Impact of image denoising on image quality, quantitative parameters and sensitivity of ultra-low-dose volume perfusion CT imaging

  • Computed Tomography
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European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To examine the impact of denoising on ultra-low-dose volume perfusion CT (ULD-VPCT) imaging in acute stroke.

Methods

Simulated ULD-VPCT data sets at 20 % dose rate were generated from perfusion data sets of 20 patients with suspected ischemic stroke acquired at 80 kVp/180 mAs. Four data sets were generated from each ULD-VPCT data set: not-denoised (ND); denoised using spatiotemporal filter (D1); denoised using quanta-stream diffusion technique (D2); combination of both methods (D1 + D2). Signal-to-noise ratio (SNR) was measured in the resulting 100 data sets. Image quality, presence/absence of ischemic lesions, CBV and CBF scores according to a modified ASPECTS score were assessed by two blinded readers.

Results

SNR and qualitative scores were highest for D1 + D2 and lowest for ND (all p ≤ 0.001). In 25 % of the patients, ND maps were not assessable and therefore excluded from further analyses. Compared to original data sets, in D2 and D1 + D2, readers correctly identified all patients with ischemic lesions (sensitivity 1.0, kappa 1.0). Lesion size was most accurately estimated for D1 + D2 with a sensitivity of 1.0 (CBV) and 0.94 (CBF) and an inter-rater agreement of 1.0 and 0.92, respectively.

Conclusion

An appropriate combination of denoising techniques applied in ULD-VPCT produces diagnostically sufficient perfusion maps at substantially reduced dose rates as low as 20 % of the normal scan.

Key Points

• Perfusion-CT is an accurate tool for the detection of brain ischemias.

• The high associated radiation doses are a major drawback of brain perfusion CT.

• Decreasing tube current in perfusion CT increases image noise and deteriorates image quality.

• Combination of different image-denoising techniques produces sufficient image quality from ultra-low-dose perfusion CT.

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Abbreviations

ACA:

anterior cerebral artery

CBF:

cerebral blood flow

CBV:

cerebral blood volume

CTA:

CT-angiography

D1:

denoised using a spatiotemporal filter

D2:

denoised using quanta-stream diffusion technique

D1 + D2:

denoised using a combination of spatiotemporal filter and quanta-stream diffusion technique

MTT:

mean transit time

ND:

not-denoised

NECT:

non-enhanced CT

NVT:

nonviable tissue

PCA:

posterior cerebral artery

rm-ANOVA:

repeated measures ANOVA

ROI:

region of interest

SD:

standard deviation

SNR:

signal-to-noise ratio

TAR:

tissue at risk

TTD:

time to drain

TTP:

time to peak

ULD-VPCT:

ultra-low-dose volume perfusion CT

VPCT:

volume perfusion CT

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Acknowledgments

The scientific guarantor of this publication is Martin Wiesmann, MD. 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. This work was supported by the Converging Research Center Program through the Ministry of Science, ICT, and Future Planning, Korea (2013K000423). This work was also supported in part by the R&D program of MKE/KEIT, Korea (10042697). No complex statistical methods were necessary for this paper. Institutional review board approval was obtained. Written informed consent was waived by the institutional review board. Methodology: retrospective, experimental, performed at one institution.

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Authors and Affiliations

  1. Department of Diagnostic and Interventional Neuroradiology, RWTH Aachen University, 52074, Aachen, Germany

    Ahmed E. Othman, Carolin Brockmann, Saif Afat, Rastislav Pjontek, Omid Nikoubashman, Marc A. Brockmann & Martin Wiesmann

  2. Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, University Hospital Tuebingen, 72076, Tübingen, Germany

    Ahmed E. Othman & Konstantin Nikolaou

  3. Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Suwon, 433-270, South Korea

    Zepa Yang, Changwon Kim & Jong Hyo Kim

  4. Department of Radiology, Seoul National University College of Medicine, Seoul, 110-744, South Korea

    Zepa Yang, Changwon Kim & Jong Hyo Kim

  5. Center for Medical-IT Convergence Technology Research, Advanced Institute of Convergence Technology, Suwon, 433-270, South Korea

    Jong Hyo Kim

  6. Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Chongno-gu, Seoul, 110-744, South Korea

    Jong Hyo Kim

Authors
  1. Ahmed E. Othman
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  2. Carolin Brockmann
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  3. Zepa Yang
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  4. Changwon Kim
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  5. Saif Afat
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  6. Rastislav Pjontek
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  7. Omid Nikoubashman
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  8. Marc A. Brockmann
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  9. Konstantin Nikolaou
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  10. Martin Wiesmann
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  11. Jong Hyo Kim
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Corresponding author

Correspondence to Jong Hyo Kim.

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Othman, A.E., Brockmann, C., Yang, Z. et al. Impact of image denoising on image quality, quantitative parameters and sensitivity of ultra-low-dose volume perfusion CT imaging. Eur Radiol 26, 167–174 (2016). https://doi.org/10.1007/s00330-015-3853-6

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  • DOI: https://doi.org/10.1007/s00330-015-3853-6

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