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

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.