Improved residue function and reduced flow dependence in MR perfusion using least‐absolute‐deviation regularization

Abstract

Cerebral blood flow (CBF) estimates derived from singular value decomposition (SVD) of time intensity curves from Gadolinium bolus perfusion-weighted imaging are known to underestimate CBF, especially at high flow rates. We report the development of a model-independent delay-invariant deconvolution technique using least-absolute-deviation (LAD) regularization to improve the CBF estimation accuracy. Computer simulations were performed to compare the accuracy of CBF estimates derived from LAD, reformulated SVD (rSVD) and standard SVD (sSVD) techniques. Simulations were performed at image signal-to-noise ratios ranging from 20 to 400, cerebral blood volumes from 1% to 10%, and CBF from 2.5 mL/100 g/min to 176.5 mL/100 g/min to estimate the effect of these parameters on the accuracy of CBF estimation. The LAD method improved the CBF estimation accuracy by up to 32% in gray matter and 23% in white matter compared with rSVD and sSVD methods.

Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma

“…Apparent diffusion coefficient (ADC) maps were created from the DWI. Relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time (MTT) maps were produced using previously published least absolute deviation (LAD) method (29).…”

Technical Pitfalls of Signal Truncation in Perfusion MRI of Glioblastoma

“…Among the three cerebral hemodynamics parameters derived from DSC perfusion: rCBV, relative cerebral blood flow (rCBF), and mean transit time (MTT), rCBV is the most stable, as it is computed by the area under the curve of the contrast concentration time course normalized to the area under the arterial input function (AIF). There are various methods, including deconvolution-based methods ( 8 – 10 ) and the tissue residue function model-based method ( 11 ), to derive rCBF and MTT. Due to the impulse response nature of the tissue residue function, which has a sharp rising edge at contrast arrival, and the limited temporal resolution, all of these methods will underestimate the peak of the tissue residue function, which leads to severe underestimation of rCBF at high flow rate compared to lower flow rate ( 8 – 10 ).…”

“…There are various methods, including deconvolution-based methods ( 8 – 10 ) and the tissue residue function model-based method ( 11 ), to derive rCBF and MTT. Due to the impulse response nature of the tissue residue function, which has a sharp rising edge at contrast arrival, and the limited temporal resolution, all of these methods will underestimate the peak of the tissue residue function, which leads to severe underestimation of rCBF at high flow rate compared to lower flow rate ( 8 – 10 ). One way to resolve this issue is to have an accurate estimation of bolus arrival time ( 12 ), removing one parameter from the deconvolution of the tissue residue function, as a small error in the estimation of bolus arrival time can change the rCBF estimates significantly ( 13 ).…”

Improving low-dose blood–brain barrier permeability quantification using sparse high-dose induced prior for Patlak model

“…However the associated increased noise in the sinogram will unavoidably lead to quality degradation and image artifacts in the reconstructed image series and hemodynamic parameter maps. Numerous approaches have been proposed to reduce the noise in the low-dose PCT data, including denoising the sinogram and/or reconstructed image series (Mendrik et al, 2011; Ma et al, 2011, 2012; Saito et al, 2008; Lin et al, 2001) and regularizing the residue functions in the deconvolution process (Calamante et al, 2003; Nathan et al, 2008; Andersen et al, 2002; Wong et al, 2009; He et al, 2010; Fang et al, 2012, 2013). However most of these approaches are not addressing the optimization of BBBP map specifically.…”