Biomedical Applications in Molecular, Structural, and Functional Imaging

Fast nonlinear regression method for CT brain perfusion analysis

[+] Author Affiliations
Edwin Bennink, Jaap Oosterbroek, Hugo W. A. M. de Jong

University Medical Center Utrecht, Department of Radiology, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands

University Medical Center Utrecht, Image Sciences Institute, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands

Kohsuke Kudo

Hokkaido University Hospital, Department of Diagnostic and Interventional Radiology, N14 W5, Kita-ku, Sapporo 060-8648, Japan

Max A. Viergever

University Medical Center Utrecht, Image Sciences Institute, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands

Birgitta K. Velthuis

University Medical Center Utrecht, Department of Radiology, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands

J. Med. Imag. 3(2), 026003 (Jun 16, 2016). doi:10.1117/1.JMI.3.2.026003
History: Received July 28, 2015; Accepted May 26, 2016
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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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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Edwin Bennink ; Jaap Oosterbroek ; Kohsuke Kudo ; Max A. Viergever ; Birgitta K. Velthuis, et al.
"Fast nonlinear regression method for CT brain perfusion analysis", J. Med. Imag. 3(2), 026003 (Jun 16, 2016). ; http://dx.doi.org/10.1117/1.JMI.3.2.026003


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