Fast nonlinear regression method for CT brain perfusion analysis

Edwin Bennink; Jaap Oosterbroek; Kohsuke Kudo; Max A. Viergever; Birgitta K. Velthuis M.D.; Hugo W. A. M. de Jong

doi:10.1117/1.JMI.3.2.026003

16 June 2016 Fast nonlinear regression method for CT brain perfusion analysis

Edwin Bennink, Jaap Oosterbroek, Kohsuke Kudo, Max A. Viergever, Birgitta K. Velthuis M.D., Hugo W. A. M. de Jong

Author Affiliations +

Journal of Medical Imaging, Vol. 3, Issue 2, 026003 (June 2016). https://doi.org/10.1117/1.JMI.3.2.026003

Abstract

Although computed tomography (CT) perfusion (CTP) imaging enables rapid diagnosis and prognosis of ischemic stroke, current CTP analysis methods have several shortcomings. We propose a fast nonlinear regression method with a box-shaped model (boxNLR) that has important advantages over the current state-of-the-art method, block-circulant singular value decomposition (bSVD). These advantages include improved robustness to attenuation curve truncation, extensibility, and unified estimation of perfusion parameters. The method is compared with bSVD and with a commercial SVD-based method. The three methods were quantitatively evaluated by means of a digital perfusion phantom, described by Kudo et al. and qualitatively with the aid of 50 clinical CTP scans. All three methods yielded high Pearson correlation coefficients (<0.9) with the ground truth in the phantom. The boxNLR perfusion maps of the clinical scans showed higher correlation with bSVD than the perfusion maps from the commercial method. Furthermore, it was shown that boxNLR estimates are robust to noise, truncation, and tracer delay. The proposed method provides a fast and reliable way of estimating perfusion parameters from CTP scans. This suggests it could be a viable alternative to current commercial and academic methods.

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Edwin Bennink, Jaap Oosterbroek, Kohsuke Kudo, Max A. Viergever, Birgitta K. Velthuis M.D., and Hugo W. A. M. de Jong "Fast nonlinear regression method for CT brain perfusion analysis," Journal of Medical Imaging 3(2), 026003 (16 June 2016). https://doi.org/10.1117/1.JMI.3.2.026003

Published: 16 June 2016

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