In order to obtain cerebral blood flow (CBF) measurements in absolute units using ASL, control vs label difference images are normalized by the equilibrium magnetization of arterial blood (M_0a)^1,2. This is usually estimated from a calibration image that is separately acquired, by extrapolating the value of M_0a from the equilibrium magnetization measured in a tissue (M_0t). In pulsed ASL (PASL) acquisitions at multiple post-labeling-delays (PLD) with PICORE labeling, the control images follow a saturation-recovery curve, allowing the estimation of M_0t and T₁ maps without the need of an extra scan. M_0a can then be computed using different methods, either based on a reference tissue, particularly cerebrospinal fluid (CSF) or white matter (WM), or on the voxelwise M_0t values. Although different calibration methods have been previously compared^1,2, their impact on the reproducibility of CBF measurements has not yet been reported. Moreover, no systematic comparison of the results obtained using control saturation-recovery data in multiple-PLD PASL has been performed. Here, we investigate the impact of different calibration methods on multiple-PLD PASL on CBF quantification and reproducibility.

Nine healthy volunteers were studied on a 3T Siemens Verio system equipped with a 12-channel-receive head RF coil in two sessions. ASL images with 9 contiguous axial slices with 3.5x3.5x7.0mm³ resolution were obtained using PICORE-PASL with TR/TE=2500ms/19ms, Q2TIPS saturation limiting the labeling bolus width to 750ms, 11 PLD values between 400 and 2400ms, in steps of 200ms, and 8 label/control repetitions for each PLD. Two PASL datasets were acquired with/without macroflow crushing (crushed/non-crushed)³. A 1mm isotropic resolution T1-weighted structural image was collected using MPRAGE. ASL data pre-processing included: motion and slice-time correction; control magnetization averaging at each PLD (control time series); and control-label magnetization subtraction and averaging at each PLD (difference time series). M_0t and T₁ maps were obtained by fitting a saturation-recovery curve to the control time series. T₁ maps were used for co-registration with the structural image, which was segmented into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). An extended kinetic model with/without intravascular arterial compartment was fitted to the non-crushed/crushed difference time series using BASIL^4,5, with T_1a=1.6s, T_1t=1.3s, τ=750ms, and α=0.9. Calibration of CBF estimates was then performed using the following three methods:

Method 1) Reference Tissue:

Method 1.1) CSF: $$$M_{0a}=(<M_0>_{CSF}\times \exp(TE\div T^*_{2CSF}-TE\div T^*_{2a}))\div λ_{CSF}$$$, where T*_2a=50ms, T*_2CSF=400ms and λ_CSF=1.15

Method 1.2) WM: $$$M_{0a}=(<M_0>_{WM}\times \exp(TE\div T^*_{2WM}-TE\div T^*_{2a}))\div λ_{WM}$$$, where T*_2a=50ms, T*_2WM=50ms and λ_WM =0.82

Method 2) Voxelwise: $$$M_{0aWM}(i)=M_{0WM}(i)\div<λ>_{WM}$$$, with <λ>_WM=0.82 and $$$M_{0aGM}(i)=M_{0GM}(i)\div<λ>_{GM}$$$, with <λ>_GM= 0.98

The median of CBF across GM and WM, and their GM-to-WM ratio, were computed in each dataset. Repeated measures ANOVA was performed across subjects to test for the effects of calibration method and data type. Reproducibility of GM CBF measurements was assessed by computing the inter- and intra-subject coefficients of variation (CV_inter and CV_intra) and the two-way mixed intraclass correlation coefficient (ICC)³. A GM region was defined by intersection between the subject’s GM mask and 9 MNI GM regions of interest (ROIs). Repeated measures ANOVA was performed across the GM ROIs to test for the effects of calibration method and data type.

Representative CBF maps obtained using the three calibration methods are shown in Fig.1. The subject and group averages of CBF in GM and WM, as well as the GM-to-WM CBF ratio, are displayed in Fig.2. Significant differences were observed in GM CBF between the voxelwise and the reference tissue methods, with voxelwise calibration producing a greater ratio than the reference tissue methods. The reproducibility results for the GM CBF measures are presented in Fig.3. All CV and ICC values are within the intervals of good/acceptable reproducibility (CVi_ntra<33% and ICC>0.4)⁴. A main effect of method was found for CVinter and CVintra, with the voxelwise method in general producing the lowest variability. No effects were found for ICC, which can be explained by the interplay between the reductions in both inter- and intra-subject variability.We found that voxelwise calibration produced significantly reduced inter- and intra-subject variability in GM CBF measurements relative to methods based on a reference tissue, when using M_0t maps estimated from the control saturation-recovery curve in multiple-PLD PICORE-PASL. Besides the reduced variability, the voxelwise method is also advantageous because it takes into account RF inhomogeneity and differences in transverse relaxation, and it does not depend on segmentation to obtain a reference tissue mask. Although the ASL white paper recommends voxelwise calibration for single-PLD pseudo-continuous ASL⁶, our results further indicate that a voxelwise approach is also preferable in multiple-PLD PASL when using the saturation-recovery curve of the control images for M_0t estimation.