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Three‐dimensional whole‐brain perfusion quantification using pseudo‐continuous arterial spin labeling MRI at multiple post‐labeling delays: accounting for both arterial transit time and impulse response function
Author(s) -
Qin Qin,
Huang Alan J.,
Hua Jun,
Desmond John E.,
Stevens Robert D.,
Zijl Peter C. M.
Publication year - 2014
Publication title -
nmr in biomedicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.278
H-Index - 114
eISSN - 1099-1492
pISSN - 0952-3480
DOI - 10.1002/nbm.3040
Subject(s) - cerebral blood flow , monte carlo method , arterial spin labeling , estimation theory , impulse (physics) , voxel , goodness of fit , perfusion scanning , mathematics , perfusion , nuclear magnetic resonance , physics , nuclear medicine , computer science , algorithm , statistics , medicine , artificial intelligence , radiology , cardiology , quantum mechanics
Measurement of the cerebral blood flow (CBF) with whole‐brain coverage is challenging in terms of both acquisition and quantitative analysis. In order to fit arterial spin labeling‐based perfusion kinetic curves, an empirical three‐parameter model which characterizes the effective impulse response function (IRF) is introduced, which allows the determination of CBF, the arterial transit time (ATT) and T 1,eff . The accuracy and precision of the proposed model were compared with those of more complicated models with four or five parameters through Monte Carlo simulations. Pseudo‐continuous arterial spin labeling images were acquired on a clinical 3‐T scanner in 10 normal volunteers using a three‐dimensional multi‐shot gradient and spin echo scheme at multiple post‐labeling delays to sample the kinetic curves. Voxel‐wise fitting was performed using the three‐parameter model and other models that contain two, four or five unknown parameters. For the two‐parameter model, T 1,eff values close to tissue and blood were assumed separately. Standard statistical analysis was conducted to compare these fitting models in various brain regions. The fitted results indicated that: (i) the estimated CBF values using the two‐parameter model show appreciable dependence on the assumed T 1,eff values; (ii) the proposed three‐parameter model achieves the optimal balance between the goodness of fit and model complexity when compared among the models with explicit IRF fitting; (iii) both the two‐parameter model using fixed blood T 1 values for T 1,eff and the three‐parameter model provide reasonable fitting results. Using the proposed three‐parameter model, the estimated CBF (46 ± 14 mL/100 g/min) and ATT (1.4 ± 0.3 s) values averaged from different brain regions are close to the literature reports; the estimated T 1,eff values (1.9 ± 0.4 s) are higher than the tissue T 1 values, possibly reflecting a contribution from the microvascular arterial blood compartment.