Fast adaptive regularization for perfusion parameter computation: Tuning the Tikhonov regularization parameter to the SNR by regression

Manhart M, Maier A, Hornegger J, Dörfler A (2015)

Publication Type: Conference contribution

Publication year: 2015

Journal

Publisher: Kluwer Academic Publishers

Pages Range: 311-316

Conference Proceedings Title: Informatik aktuell

Event location: Lubeck, DEU

ISBN: 9783662462232

DOI: 10.1007/978-3-662-46224-9_54

Abstract

Computation of perfusion parameters by deconvolution from contrast-enhanced time-resolved CT or MR perfusion data sets is an illconditioned problem. Thus, adequate regularization and determination of corresponding regularization parameters is required. We present a novel method for Tikhonov regularization for perfusion imaging to locally adapt parameters to the SNR level by using a regression function. In an numerical evaluation our simple approach provided similar or even superior results compared to methods applying computationally more demanding L-curve analysis.

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How to cite

APA:

Manhart, M., Maier, A., Hornegger, J., & Dörfler, A. (2015). Fast adaptive regularization for perfusion parameter computation: Tuning the Tikhonov regularization parameter to the SNR by regression. In Thomas Martin Deserno, Thomas Tolxdorff, Heinz Handels, Hans-Peter Meinzer (Eds.), Informatik aktuell (pp. 311-316). Lubeck, DEU: Kluwer Academic Publishers.

MLA:

Manhart, Michael, et al. "Fast adaptive regularization for perfusion parameter computation: Tuning the Tikhonov regularization parameter to the SNR by regression." Proceedings of the Workshops on Image Processing for Medicine,2015:Algorthim-Systems-Applications, Lubeck, DEU Ed. Thomas Martin Deserno, Thomas Tolxdorff, Heinz Handels, Hans-Peter Meinzer, Kluwer Academic Publishers, 2015. 311-316.

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