Optimized super-selective Arterial Spin Labeling for quantitative flow territory mapping.

Area of Science:

• Neuroimaging
• Medical Physics
• Physiology

Background:

• Arterial Spin Labeling (ASL) provides insights into brain physiology, but relies on assumptions of labeling efficiency for perfusion calculations.
• Inaccuracies can arise from these assumptions, particularly in super-selective ASL where precise arterial targeting is challenging.
• Accurate measurement of labeling efficiency is vital for reliable ASL-derived brain perfusion data.

Purpose of the Study:

• To present an optimized super-selective ASL tagging scheme.
• To measure and evaluate the labeling efficiency of this super-selective ASL approach.
• To assess the impact of labeling efficiency on cerebral blood flow quantification, especially when the labeling spot deviates from the target artery.

Main Methods:

• Developed and implemented an optimized super-selective ASL tagging scheme.
• Measured labeling efficiency using quantitative phase-contrast angiography of the tagged artery and flow territory volume.
• Compared measured efficiencies with simulations and evaluated cerebral blood flow accuracy with shifted labeling foci.

Main Results:

• The study presents an optimized super-selective ASL method with measured labeling efficiencies.
• Cerebral blood flow quantification remained accurate despite shifts in the labeling focus (up to 3 mm), provided labeling efficiency was considered.
• Signal-to-noise ratio (SNR) was expectedly lower with focus shifts but did not compromise accuracy when efficiency was accounted for.

Conclusions:

• Measuring labeling efficiency is essential for accurate super-selective ASL perfusion quantification.
• Accounting for measured labeling efficiency prevents false results and ensures reliable cerebral blood flow values.
• The optimized super-selective ASL approach demonstrates robustness in maintaining accuracy even with minor positional deviations.