Robust estimation of quantitative perfusion from multi-phase pseudo-continuous arterial spin labeling

Abstract

Purpose: Multi-phase PCASL has been proposed as a means to achieve accurate perfusion quantification that is robust to imperfect shim in the labeling plane. However, there exists a bias in the estimation process that is a function of noise in the data. In this work, this bias is characterized and then addressed in animal and human data. Methods: The proposed algorithm to overcome bias uses the initial biased voxel-wise estimate of phase tracking error to cluster regions with different off-resonance phase shifts, from which a high-SNR estimate of regional phase offset is derived. Simulations were used to predict the bias expected at typical SNR. Multi-phase PCASL in 3 rat strains (n = 21) at 9.4 T was considered, along with 20 human subjects previously imaged using ASL at 3 T. The algorithm was extended to include estimation of arterial blood flow velocity. Results: Based on simulations, a perfusion estimation bias of 6-8% was expected using 8-phase data at typical SNR. This bias was eliminated when a high-precision estimate of phase error was available. In the preclinical data, the bias-corrected measure of perfusion (107 ± 14 mL/100g/min) was lower than the standard analysis (116 ± 14 mL/100g/min), corresponding to a mean observed bias across strains of 8.0%. In the human data, bias correction resulted in a 15% decrease in the estimate of perfusion. Conclusions: Using a retrospective algorithmic approach, it was possible to exploit common information found in multiple voxels within a whole region of the brain, offering superior SNR and thus overcoming the bias in perfusion quantification from multi-phase PCASL.