"Subject-specific optimization of background suppression for arterial spin labeling MRI using a feedback loop on the scanner"

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

Abstract

Background suppression (BGS) in arterial spin labeling (ASL) MRI leads to a higher temporal SNR (tSNR) of the perfusion images compared to ASL without BGS. The performance of the BGS, however, depends on the tissue relaxation times and on inhomogeneities of the scanner's magnetic fields, which differ between subjects and are unknown at the moment of scanning. Therefore, we developed a feedback loop (FBL) mechanism that optimizes the BGS for each subject in the scanner during acquisition. We implemented the FBL for 2D pseudo-continuous ASL (PCASL) scans with an echo-planar imaging (EPI) readout. After each dynamic scan, acquired ASL images were automatically sent to an external computer and processed with a Python processing tool. Inversion times were optimized on-the-fly using 80 iterations of the Nelder-Mead method, by minimizing the signal intensity in the label image while maximizing the signal intensity in the perfusion image. The performance of this method was first tested in a 4-component phantom. The regularization parameter was then tuned in 6 healthy subjects (3 male, 3 female, age 24-62 years) and set as λ=4 for all other experiments. Resulting ASL images, perfusion images and tSNR maps obtained from the last 20 iterations of the FBL scan were compared to those obtained without BGS and to standard BGS in 12 healthy volunteers (5 male, 7 female, age 24-62 years) (including the 6 volunteers used for tuning of λ). The FBL resulted in perfusion images with a statistically significantly higher tSNR (2.20) compared to standard BGS (1.96) (P < 5 10^-3, two-sided paired t-test). Minimizing signal in the label image furthermore resulted in control images from which approximate changes in perfusion signal can directly be appreciated. This could be relevant to ASL applications that require a high temporal resolution. Future work is needed to minimize the number of initial acquisitions during which the performance of BGS is reduced compared to standard BGS and to extend the technique to 3D ASL.

BibTeX entry