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European Radiology

, Volume 25, Issue 8, pp 2354–2361 | Cite as

Wavelet-based calculation of cerebral angiographic data from time-resolved CT perfusion acquisitions

  • Lukas Havla
  • Kolja M. Thierfelder
  • Sebastian E. Beyer
  • Wieland H. Sommer
  • Olaf Dietrich
Computed Tomography

Abstract

Objectives

To evaluate a new approach for reconstructing angiographic images by application of wavelet transforms on CT perfusion data.

Methods

Fifteen consecutive patients with suspected stroke were examined with a multi-detector CT acquiring 32 dynamic phases (∆t = 1.5s) of 99 slices (total slab thickness 99mm) at 80kV/200mAs. Thirty-five mL of iomeprol-350 was injected (flow rate = 4.5mL/s). Angiographic datasets were calculated after initial rigid-body motion correction using (a) temporally filtered maximum intensity projections (tMIP) and (b) the wavelet transform (Paul wavelet, order 1) of each voxel time course. The maximum of the wavelet-power-spectrum was defined as the angiographic signal intensity. The contrast-to-noise ratio (CNR) of 18 different vessel segments was quantified and two blinded readers rated the images qualitatively using 5pt Likert scales.

Results

The CNR for the wavelet angiography (501.8 ± 433.0) was significantly higher than for the tMIP approach (55.7 ± 29.7, Wilcoxon test p < 0.00001). Image quality was rated to be significantly higher (p < 0.001) for the wavelet angiography with median scores of 4/4 (reader 1/reader 2) than the tMIP (scores of 3/3).

Conclusions

The proposed calculation approach for angiography data using temporal wavelet transforms of intracranial CT perfusion datasets provides higher vascular contrast and intrinsic removal of non-enhancing structures such as bone.

Key points

Angiographic images calculated with the proposed wavelet-based approach show significantly improved contrast-to-noise ratio.

CT perfusion-based wavelet angiography is an alternative method for vessel visualization.

Provides intrinsic removal of non-enhancing structures such as bone.

Keywords

X-ray computed tomography Angiography Perfusion Wavelet analysis Brain 

Notes

Acknowledgements

The scientific guarantor of this publication is Olaf Dietrich. 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. 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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Copyright information

© European Society of Radiology 2015

Authors and Affiliations

  • Lukas Havla
    • 1
  • Kolja M. Thierfelder
    • 2
  • Sebastian E. Beyer
    • 2
  • Wieland H. Sommer
    • 2
  • Olaf Dietrich
    • 1
  1. 1.Josef-Lissner-Laboratory for Biomedical Imaging, Institute for Clinical RadiologyLudwig-Maximilians-University Hospital MunichMunichGermany
  2. 2.Institute for Clinical RadiologyLudwig-Maximilians-University Hospital MunichMunichGermany

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