Rapid perfusion quantification using Welch-Satterthwaite approximation and analytical spectral filtering

Karthik Krishnan; Kasireddy V. Reddy; Bhavya Ajani; Phaneendra K. Yalavarthy

doi:10.1117/12.2249975

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24 February 2017 Rapid perfusion quantification using Welch-Satterthwaite approximation and analytical spectral filtering

Karthik Krishnan, Kasireddy V. Reddy, Bhavya Ajani, Phaneendra K. Yalavarthy

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Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 101330Q (2017) https://doi.org/10.1117/12.2249975
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

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Karthik Krishnan, Kasireddy V. Reddy, Bhavya Ajani, and Phaneendra K. Yalavarthy "Rapid perfusion quantification using Welch-Satterthwaite approximation and analytical spectral filtering", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330Q (24 February 2017); https://doi.org/10.1117/12.2249975

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