Optimising MR perfusion imaging: comparison of different software-based approaches in acute ischaemic stroke
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- Schaafs, LA., Porter, D., Audebert, H.J. et al. Eur Radiol (2016) 26: 4204. doi:10.1007/s00330-016-4244-3
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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.
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.
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).
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.
• 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.
arterial input function
cerebral blood flow
singular value decomposition with a block-circulant matrix
delay and dispersion
final lesion volume
intraclass correlation coefficient
mean transit time
singular value decomposition
standard truncated singular value decomposition