Cerebral blood flow estimation from perfusion-weighted MRI using FT-based MMSE filtering method

Abstract

Introduction-Perfusion weighted MRI can be used for estimating blood flow parameters using bolus tracking technique based on DSC MRI. In order to extract flow parameters, several deconvolution techniques have been proposed, of which, the SVD and FT based techniques are more popular and widely used. In this work, an FT based method has been proposed that involves derivation of an optimal shaped filter (defined as a filter function) estimated using minimum mean-squared error (MMSE) technique in the frequency domain. The proposed technique has been compared with the well established SVD technique using simulation experiments.

Application of FT-based MMSE deconvolution method for cerebral blood flow measurement in patients with leukoaraiosis

“…FT-based MMSE method is inherently adaptive to the noise level [2] and is more regulatory in low-flow, low-contrast, low-SNR regions so that it potentially does not overestimate the low flow values. The IQR for the NWM were comparable between the techniques.…”

“…oSVD method uses a fixed oscillation index based on certain SNR assumption, to reject singular values that will be not be used for estimating CBF [5]. On the other hand, the FT-based MMSE method involves estimation of an optimal shaped filter,ϕ*( f ), derived using the minimum mean-squared error method (please see previous publication for derivation of the shaped filter)[2]. The deconvolution methods are essentially mathematical methods used for solving for impulse-residue function.…”

“…The FT-based MMSE method used in this study for estimation of relative CBF values can be thought of as a variant of a Wiener filter[6]. However, the difference lies in the fact that, in the proposed method, SNR level is estimated for every pixel adaptively based on the model formulated in [2], the optimal shaped filter function is derived based on that estimation pixel-wise, and noise is filtered adaptively, pixel-wise. In some ways, the FT-based MMSE method can be called an adaptive Wiener filter, since it uses pixel-wise noise information to obtain a uniquely shaped filter to selectively reduce the high frequency part of the data that is assumed to be dominated by noise.…”

“…However, from the published literature and available software programs, it appears that SVD-based deconvolution method is more widely used than other deconvolution methods [1,5,8,9]. In a previous publication [2], we proposed a modified FT-based method that involves derivation of an optimally-shaped filter (defined as a function of frequency) estimated using MMSE technique in the Fourier domain. The proposed technique was compared with the well-established oSVD technique using simulation experiments.…”

Absolute quantification of perfusion using dynamic susceptibility contrast MRI: pitfalls and possibilities

“…Sakoglu et al [110] proposed a FT method including the development of a filter using the minimum mean-squared error in the frequency domain. They found that the technique was stable at low noise levels, but that it underestimated the CBF under moderate noise conditions.…”