ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion

Abstract

Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary method for noninvasively measuring regional brain perfusion in humans. We introduce ASLPrep, a suite of software pipelines that ensure the reproducible and generalizable processing of ASL MRI data.

Extended Data Fig. 1. Exemplar data for each dataset and CBF quantification method.

A single participant from each dataset is shown (in MNI space), with CBF quantified using each of four methods. SCRUB could not be applied for the FTD dataset as an ASL timeseries is required; the sequence used for that study provided only a ΔM image.

Extended Data Fig. 2. Mean cerebral blood flow maps for each dataset and quantification method.

CBF quantification for each dataset using the four methods supported by ASLPrep. An axial slice (z = 0) of the mean CBF image for each dataset is displayed (in MNI space) for each quantification method. SCRUB could not be applied for the FTD dataset as an ASL timeseries is required; the sequence used for that study provided only a single ΔM image.

Extended Data Fig. 3. Image smoothness from a legacy pipeline and ASLPrep.

a) Comparison of CBF quantified with ASLPrep and a previously published pipeline used for the PNC; both pipelines implemented the standard kinetic model. Image smoothness in template space differed between ASLPrep and the previous pipeline (two-sided t-test; t(1,480) = 252.58, p < 1 × 10⁻). b) Across-dataset average image.

Extended Data Fig. 4. CBF quantified using ASLPrep aligns with a commonly used PET atlas.

a) CBF quantified using ASLPrep (averaged across datasets) aligned with CBF from a commonly used PET atlas included in SPM, where CBF was measured using [¹⁵O]water PET (Pearson r = 0.60). b) Assessment of the similarity of the images using a null distribution (comparison with a Brainsmash null p = 0.0001; panel b).

Extended Data Fig. 5. Bayesian methods mitigate impact of in-scanner motion on CBF image quality.

Impact of motion on the CBF image quality as assessed by the Quality Evaluation Index. The impact of motion on quality differed significantly among quantification approaches (linear mixed effects model, F = 228.09, p = 1.0 × 10⁻²⁵). The envelope indicates the 95% confidence interval.

Extended Data Fig. 6. CBF of gray and white matter across datasets.

The distribution of cerebral blood flow (CBF) within grey matter (GM) and white matter (WM) is displayed for each dataset, for each quantification option: the standard CBF model, BASIL (a), BASIL with partial volume correction (PVC; b), and SCRUB (c). SCRUB could not be applied for the FTD dataset as an ASL timeseries is required; the sequence used for that study provided only a single ΔM image. Boxes within each violin plot indicate interquartile range with the median shown as a white dot.

Extended Data Fig. 7. CBF declines nonlinearly with age over the lifespan.

Evolution of gray matter CBF with age over the lifespan across all five datasets. For each dataset, we used four methods for quantifying CBF: the standard CBF model (see main text), BASIL (a), BASIL with partial volume correction (PVC; b), and SCRUB (c). We used a generalized additive model with penalized splines to characterize the nonlinear evolution of CBF over age. The thick black line represents the predicted values, while the dashed lines represent the 95% confidence intervals.

Extended Data Fig. 8. Compute time for ASLPrep.

Distribution of compute time for each dataset, separated by ASL processing and anatomic processing (which relies upon sMRIPrep). Anatomic preprocessing always required a longer duration, with ASL preprocessing and CBF computation requiring less than 70 minutes in all datasets.

Fig. 1:. Overview of ASLPrep.

Input data to ASLPrep includes ASL images, anatomical (T1 weighted) images and (optionally) M0 reference images. Anatomical preprocessing is executed using standard tools (as implemented in sMRIPrep); ASL image processing includes both preprocessing and CBF computation.

Fig. 2:. ASLPrep quantifies CBF across sequences, scanners and the lifespan.

a, CBF in GM and WM for each dataset. Boxes in each violin plot indicate interquartile range with the median shown as a white dot. b, GM CBF across the lifespan. The thick black line represents the predicted values from a generalized additive model; the dashed lines indicate the 95% confidence interval (R² = 0.57; P = 1.1 × 10⁻¹⁶).