Reference-based Maximum Upslope: A CBF Quantification Method without Using Arterial Input Function in Dynamic Susceptibility Contrast MRI

Abstract

Purpose: We assessed errors in cerebral blood ‰ow (CBF) obtained from our proposed reference-based method without using arterial input function (AIF) indices in dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI).Materials and Methods: We calculated CBF and the referential tissue-related ratio (CBFratio) using numerical simulation and 3 nondeconvolution methods and a deconvolution method of block-circulant singular value decomposition (cSVD). We compared errors with and without simulated noise as parameters of mean transit time (MTT), AIF delay and temporal resolution, and clinical DSC-MRI maps.Results: The errors in CBF obtained using maximum upslope (US) were smallest among the nondeconvolution methods and almost equivalent to errors in the cSVD method under practical imaging conditions. In addition, errors in the CBFratio obtained using referencebased US (Ref-US) referring to white matter were smallest, even compared to all errors in CBF and CBFratio. The Ref-US method introduced less error than the cSVD method, especially at low ‰ow rates, was further robust against AIF noise and coarse temporal resolution, and was comparably robust against transit delay. In pixel-by-pixel correlations between absolute value maps for US and for cSVD-CBF in clinical DSC-MRI, those correlation coe‹cients (r) between the 2 maps were stable, rÀ0.9, despite variation in the slopes of the linear regression line, so the 2 CBFratio maps were visually well correlated in any case.Conclusion: The Ref-US technique without AIF measurement can become a practical perfusion methodology for DSC-MRI in patients even with acute stroke because it balances robustness with systematic and random errors and it is simply performed.

Early time points perfusion imaging

“…There is considerable interest to compare the maximum gradient-based method (Kimura and Kusahara, 2009; Koenig et al, 1998; Miles and Griffiths, 2003) and ET. If the time taken to get to the maximum gradient is always within the time window of τ, then the maximum gradient-based method would indeed be theoretically compatible with ET.…”

“…It should be mentioned that the initial slope method bears resemblance to the maximum gradient-based method (Kimura and Kusahara, 2009; Koenig et al, 1998; Miles and Griffiths, 2003) used in Computed Tomography (CT) and MR perfusion analysis. We leave a more detailed comparison between the maximum gradient-based method and ET to the Discussion section.…”

Early time points perfusion imaging: Theoretical analysis of correction factors for relative cerebral blood flow estimation given local arterial input function

“…This maximum value of AIF (also known as the height of AIF) being used for the correction factor for MD1 at TMD1 is well established in the literature of the maximum gradient based method (Axel, 1980; Herfkens et al, 1982; Kimura and Kusahara, 2009; Miles, 1991). We are just recasting it in the framework of ET.…”

“…In the investigation of the myriad ways that can be used to estimate rCBF by ET given known local AIF information, we also obtained an answer as a byproduct of our correction factor analysis to the following interesting question: Within the framework of using the normalized gamma-variate function as a model for AIF, is the rCBF result obtained by the well established maximum gradient based method (Kimura and Kusahara, 2009; Koenig et al, 1998; Miles, 1991; Miles and Griffiths, 2003) independent of local AIF?…”

“…As long as the problem of local AIF identification has been solved, whatever results we can learn about rCBF evaluation by ET from the investigation of the local AIF question of a single individual will be directly applicable to the question of cross-subject comparison of rCBF. For cross-subject comparison by ET, we could have also considered a reference based method similar to the reference-based maximum up-slope (Ref-US) method (Kimura and Kusahara, 2009) which requires setting a region of interest (ROI) on some referential tissue (e.g. white matter ROI) but would not require any AIF information.…”