"Individually optimized ASL background suppression using a real-time feedback loop on the scanner"

Kirsten Koolstra, Marius Staring, Paul de Bruin and Matthias J.P. van Osch

Abstract

Suppression of background signal in ASL leads to perfusion images with higher signal-to-noise ratio (SNR) compared to ASL without background suppression (BGS)1. BGS is obtained by applying multiple inversion pulses before and during the post-label delay (PLD). The optimal inversion times, and therefore the quality of the BGS, depends on the relaxation times of the underlying tissue (T1, T2) and on imperfections of the scanner's magnetic fields (B0, B1+). Although this results in inter-subject differences, current ASL protocols make use of one set of predefined inversion times for all subjects, primarily because these inter-scan variations are not known at the moment of scanning. This means that the quality of the resulting perfusion images is not optimal for all subjects. In this work, we develop and implement a feedback loop that optimizes the timings of ASL BGS pulses real-time on the scanner, generating individually optimized perfusion images for each subject.

BibTeX entry