Abstract

Tissue perfusion allows for delivery of oxygen and nutrients to tissues, and in the kidneys is also a key determinant of glomerular filtration. Quantification of regional renal perfusion provides a potential window into renal (patho) physiology. However, non-invasive, practical, and robust methods to measure renal perfusion remain elusive, particularly in the clinic. Arterial spin labeling (ASL), a magnetic resonance imaging (MRI) technique, is arguably the only available method with potential to meet all these needs. Recent developments suggest its viability for clinical application. This review addresses several of these developments and discusses remaining challenges with the emphasis on renal imaging in human subjects.

Highlights

  • The delivery of oxygen and nutrients to tissues is inextricably linked to blood flow at the level of the tissue capillary bed

  • Arterial spin labeling (ASL) is an magnetic resonance imaging (MRI) technique that harnesses blood water as a freely diffusible tracer to allow a non-invasive quantification of tissue perfusion [5,6]

  • (DCE-MRI), there is conflicting evidence with with dynamic contrast-enhanced MRI (DCE-MRI), there is conflicting evidence with respect to the respect to the similarity of RBF estimates obtained with the two techniques

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Summary

Background

Checkmarks mean better for each “feature” of the readout type (e.g., 3D single-shot is most robust concerning motion artifacts, but achieves the lowest spatial resolution, due to echo train duration constraints). Checkmarks mean better for each “feature” of the readout type 2018, 8,motion concerning artifacts, but achieves the lowest spatial resolution, due to echo train duration of 15 constraints)

ASL in a Nutshell
Labeling
Readout
Modeling
Multiple
Recent Developments in Renal ASL
Validation
Comparison
Improving the Robustness of RBF Measurements
ASL at High Field
Clinical Applications
Monitoring Renal Allograft Function
Pharmacological Modulation
Subject Movement
Lack of Consensus Regarding Labeling Strategy
Readout Optimization
Quantification Model Selection
Conclusions

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