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

By David C Alsop, John A. Detre, X. Golay, M. Günther, J. Hendrikse, L. Hernandez-Garcia, Hanzhang Lu, Bradley J. MacIntosh, Laura M. Parkes, Marion Smits and 4 more. Magnetic Resonance in Medicine, 2014

2,191 citations recorded by the source corpus.

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.

Subfields: Materials Chemistry, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/673f7a07a7d2a730522b6a6af8a026b408dda197

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiology, Nuclear Medicine and Imaging

1. Vikas Gulani
2. Sébastien Ourselin
3. Mark A. Griswold
4. Ninon Burgos
5. Arvid Lundervold

Citations in the corpus

• MR Vascular Fingerprinting in Stroke and Brain Tumors Models (2016, methodology): “Yet, a number of technical issues based on these approaches are still under investigation after almost 20 years of development, such as: (1) quantification remains a challenge; (2) results obtained in normal tissues are difficult to reproduce in pathological environments; and (3) measurements tend t”
• DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual Learning (2018, methodology): “The difference reflects the perfusion, which can be quantified via appropriate modelling [1,10].”
• Reduced acquisition time PET pharmacokinetic modelling using simultaneous ASL–MRI: proof of concept (2018, methodology): “85 ( ) as recommended in the ASL consensus paper.(23) S is the signal difference between the control and label images, S0 maps were estimated by fitting saturation recovery images acquired with the same sequence at three different saturation times (1,2,4 s) using NiftyFit.”
• An a contrario approach for the detection of patient-specific brain perfusion abnormalities with arterial spin labelling (2016, neutral): “Recent literature (e.g. (Villien et al., 2012), (Alsop et al., 2014)) recommends using a shorter bolus width than the one we typically used in this experiment.”
• Estimation of Perfusion Properties with MR Fingerprinting Arterial Spin Labeling (2018, methodology): “Additionally, the tissue property values seen in the grey matter ROI analyses (Table 1) and in the property maps were similar to those reported in the literature [26,33].”