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Proceedings Paper

Rapid perfusion quantification using Welch-Satterthwaite approximation and analytical spectral filtering
Author(s): Karthik Krishnan; Kasireddy V. Reddy; Bhavya Ajani; Phaneendra K. Yalavarthy
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Paper Abstract

CT and MR perfusion weighted imaging (PWI) enable quantification of perfusion parameters in stroke studies. These parameters are calculated from the residual impulse response function (IRF) based on a physiological model for tissue perfusion. The standard approach for estimating the IRF is deconvolution using oscillatory-limited singular value decomposition (oSVD) or Frequency Domain Deconvolution (FDD). FDD is widely recognized as the fastest approach currently available for deconvolution of CT Perfusion/MR PWI. In this work, three faster methods are proposed. The first is a direct (model based) crude approximation to the final perfusion quantities (Blood flow, Blood volume, Mean Transit Time and Delay) using the Welch-Satterthwaite approximation for gamma fitted concentration time curves (CTC). The second method is a fast accurate deconvolution method, we call Analytical Fourier Filtering (AFF). The third is another fast accurate deconvolution technique using Showalter’s method, we call Analytical Showalter’s Spectral Filtering (ASSF). Through systematic evaluation on phantom and clinical data, the proposed methods are shown to be computationally more than twice as fast as FDD. The two deconvolution based methods, AFF and ASSF, are also shown to be quantitatively accurate compared to FDD and oSVD.

Paper Details

Date Published: 24 February 2017
PDF: 16 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330Q (24 February 2017); doi: 10.1117/12.2249975
Show Author Affiliations
Karthik Krishnan, Samsung R&D Institute (India)
Kasireddy V. Reddy, Samsung R&D Institute (India)
Bhavya Ajani, Samsung R&D Institute (India)
Phaneendra K. Yalavarthy, Indian Institute of Science (India)


Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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