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Optimising MR perfusion imaging: comparison of different software-based approaches in acute ischaemic stroke

Abstract

Objectives

Perfusion imaging (PI) is susceptible to confounding factors such as motion artefacts as well as delay and dispersion (D/D). We evaluate the influence of different post-processing algorithms on hypoperfusion assessment in PI analysis software packages to improve the clinical accuracy of stroke PI.

Methods

Fifty patients with acute ischaemic stroke underwent MRI imaging in the first 24 h after onset. Diverging approaches to motion and D/D correction were applied. The calculated MTT and CBF perfusion maps were assessed by volumetry of lesions and tested for agreement with a standard approach and with the final lesion volume (FLV) on day 6 in patients with persisting vessel occlusion.

Results

MTT map lesion volumes were significantly smaller throughout the software packages with correction of motion and D/D when compared to the commonly used approach with no correction (p = 0.001–0.022). Volumes on CBF maps did not differ significantly (p = 0.207–0.925). All packages with advanced post-processing algorithms showed a high level of agreement with FLV (ICC = 0.704–0.879).

Conclusions

Correction of D/D had a significant influence on estimated lesion volumes and leads to significantly smaller lesion volumes on MTT maps. This may improve patient selection.

Key Points

Assessment on hypoperfusion using advanced post-processing with correction for motion and D/D.

CBF appears to be more robust regarding differences in post-processing.

Tissue at risk is estimated more accurately by correcting software algorithms.

Advanced post-processing algorithms show a higher agreement with the final lesion volume.

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Fig. 1

Abbreviations

AIF:

arterial input function

CBF:

cerebral blood flow

cSVD:

singular value decomposition with a block-circulant matrix

D/D:

delay and dispersion

FLV:

final lesion volume

ICC:

intraclass correlation coefficient

MTT:

mean transit time

SVD:

singular value decomposition

sSVD:

standard truncated singular value decomposition

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Acknowledgments

The scientific guarantor of this publication is PD Dr. Jochen Fiebach. Jochen B. Fiebach reports receiving consulting, lecture and advisory board fees from BMS, Siemens, Perceptive, Synarc, BioImaging Technologies, Novartis, Wyeth, Pfizer, Boehringer Ingelheim, Lundbeck and Sygnis. This study received funding from the Federal Ministry of Education and Research via the Center for Stroke Research Berlin grant (01EO0801 and 01EO01301). One of the authors has significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Methodology: prospective and retrospective, observational, performed at one institution. Parts of this study are included in the medical thesis of Lars-Arne Schaafs.

Author information

Correspondence to Lars-Arne Schaafs.

Additional information

Jochen B. Fiebach and Kersten Villringer contributed equally to this work.

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Schaafs, L., Porter, D., Audebert, H.J. et al. Optimising MR perfusion imaging: comparison of different software-based approaches in acute ischaemic stroke. Eur Radiol 26, 4204–4212 (2016). https://doi.org/10.1007/s00330-016-4244-3

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Keywords

  • MRI
  • Perfusion
  • Stroke
  • Software
  • Image processing