Modeling dispersion in arterial spin labeling: validation using dynamic angiographic measurements

Area of Science:

• Medical Imaging
• Biophysics
• Vascular Biology

Background:

• Arterial spin labeling (ASL) MRI perfusion quantification relies on assumptions about label signal arrival in capillaries.
• The ideal "square" label profile is altered by transit through vasculature due to dispersion effects.
• Existing models for dispersion are challenging to validate experimentally.

Purpose of the Study:

• To compare and validate different models of label dispersion in ASL.
• To identify the most accurate and practical model for ASL perfusion quantification.

Main Methods:

• ASL data were acquired with the label remaining in larger arteries.
• Probabilistic algorithms were used to fit various dispersion models to the acquired data.
• Model performance was evaluated based on data fit and consistency with physiological flow.

Main Results:

• A model employing a gamma distribution as a dispersion kernel provided the best fit to ASL data.
• This gamma distribution model accurately reflected measured arterial flow profiles.
• The chosen model demonstrated mathematical simplicity, making it suitable for clinical ASL quantification.

Conclusions:

• Modeling ASL dispersion via convolution with a gamma distribution kernel is the most appropriate approach.
• This method improves the accuracy of ASL MRI perfusion quantification.
• The validated model is practical for routine clinical application in assessing tissue perfusion.