Recommended implementation of arterial spin‐labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia

This review provides a summary statement of recommended implementations of arterial spin labeling (ASL) for clinical applications. It is a consensus of the ISMRM Perfusion Study Group and the European ASL in Dementia consortium, both of whom met to reach this consensus in October 2012 in Amsterdam. Although ASL continues to undergo rapid technical development, we believe that current ASL methods are robust and ready to provide useful clinical information, and that a consensus statement on recommended implementations will help the clinical community to adopt a standardized approach. In this review, we describe the major considerations and trade‐offs in implementing an ASL protocol and provide specific recommendations for a standard approach. Our conclusion is that as an optimal default implementation, we recommend pseudo‐continuous labeling, background suppression, a segmented three‐dimensional readout without vascular crushing gradients, and calculation and presentation of both label/control difference images and cerebral blood flow in absolute units using a simplified model. Magn Reson Med 73:102–116, 2015. © 2014 Wiley Periodicals, Inc.     

Accurate,localized quantification of white matter perfusion with single‐voxel ASL

Quantification of perfusion in white matter is still difficult due to its low level, causing an often insufficiently low signal‐to‐noise ratio, and its long and inhomogeneous transit delays. Here, a technique is presented that accurately measures white matter perfusion by combining a spectroscopic single‐voxel localization technique (point‐resolved spectroscopy) with a pulsed arterial spin labeling encoding scheme (flow‐sensitive alternating inversion recovery) to specifically address the properties of white matter. The transit delay was measured by shifting the position of a slice‐selective saturation pulse between inversion and acquisition. Perfusion measurements resulted in values of 15.6 ± 3.2 mL/100 g/min in the left and 15.2 ± 4.8 mL/100 g/min in the right hemispheric white matter and 83.2 ± 15.2 mL/100 g/min in cortical gray matter. Taking dispersion of the transit times into account does not cause a significant change in the measured values. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.     

Comparing model‐based and model‐free analysis methods for QUASAR arterial spin labeling perfusion quantification

Amongst the various implementations of arterial spin labeling MRI methods for quantifying cerebral perfusion, the QUASAR method is unique. By using a combination of labeling with and without flow suppression gradients, the QUASAR method offers the separation of macrovascular and tissue signals. This permits local arterial input functions to be defined and “model‐free” analysis, using numerical deconvolution, to be used. However, it remains unclear whether arterial spin labeling data are best treated using model‐free or model‐based analysis. This work provides a critical comparison of these two approaches for QUASAR arterial spin labeling in the healthy brain. An existing two‐component (arterial and tissue) model was extended to the mixed flow suppression scheme of QUASAR to provide an optimal model‐based analysis. The model‐based analysis was extended to incorporate dispersion of the labeled bolus, generally regarded as the major source of discrepancy between the two analysis approaches. Model‐free and model‐based analyses were compared for perfusion quantification including absolute measurements, uncertainty estimation, and spatial variation in cerebral blood flow estimates. Major sources of discrepancies between model‐free and model‐based analysis were attributed to the effects of dispersion and the degree to which the two methods can separate macrovascular and tissue signal. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.     

Reduction of errors in ASL cerebral perfusion and arterial transit time maps using image de‐noising

In this work, the performance of image de‐noising techniques for reducing errors in arterial spin labeling cerebral blood flow and arterial transit time estimates is investigated. Simulations were used to show that the established arterial spin labeling cerebral blood flow quantification method exhibits the bias behavior common to nonlinear model estimates, and as a result, the reduction of random errors using image de‐noising can improve accuracy. To assess the effect on precision, multiple arterial spin labeling data sets acquired from the rat brain were processed using a variety of common de‐noising methods (Wiener filter, anisotropic diffusion filter, gaussian filter, wavelet decomposition, and independent component analyses). The various de‐noising schemes were also applied to human arterial spin labeling data to assess the possible extent of structure degradation due to excessive spatial smoothing. The animal experiments and simulated data show that noise reduction methods can suppress both random and systematic errors, improving both the precision and accuracy of cerebral blood flow measurements and the precision of transit time maps. A number of these methods (and particularly independent component analysis) were shown to achieve this aim without compromising image contrast. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.