The BASIL command line tool performs kinetic model inversion on ASL label-control difference data. It uses a common Bayesian inference method regardless of whether the data contains a single or multiple post labelling delays. BASIL includes a flexibly defined kinetic model appropriate for ASL kinetics that can be applied in humans and also other species - for more information see the model section.
To run BASIL on resting-state ASL data you will need:
- ASL difference data (single or multiple post labeling delays)
- differencing of label and control images should have been done
- differencing of label and control images should have been done already, see
- Details of the sequence, i.e. post-lableing delay(s), bolus duration, etc.
BASIL runs in multiple steps increasing the model complexity at each stage, this ensures a more robust final result by ensuring good convergence upon the global solution in all voxels. In general we recommend including the spatial prior/regularisation option that BASIL offers, this is run as a final step.
A rough overview of the process would be:
- STEP 1: Bayesian inference - Inference for CBF and arrival time (and optionally label duration)
- STEP 2+: Bayesian inference - further parameters of the model can be inferred from the data.
- STEP N: Bayesian inference with spatial prior - a final run for all the parameters inlcuding a spatial prior on the perfusion parameter, initalised by the prior step.
oxford_asl the data analysis using
basil is often run
twice: firstly on the data where the different repeats at the various
PLD have been averaged, and then on the full data using the output of
the first run to initialise the second. This is for similar reasons of
robutness and encouraging good convergence as the multi-step process
outlined above. This is not a default behviour of the
command line tool, but can be achiueve using the
The Kinetic curve model for resting state ASL is built into FABBER and called by BASIL. The model implemented follows the ‘standard’ or ‘Buxton’ model, for more information see:
Buxton, R. B., L. R. Frank, et al., ‘A general kinetic model for quantitative perfusion imaging with arterial spin labeling’, Magnetic Resonance in Medicine 40(3): 383-396, 1998.
As per this paper, BASIL assumes by default a single well-mixed tissue compartment and no dispersion of the bolus of labeled blood water. Different T1 values are assumed for blood and tissue water, but it is posisble to set these to be identical to match the simple model assumed by the quantification formula in the ‘white paper’.
BASIL also implements a range of alternative arterial input functions - to model disperion - and residue functions - to model restricted water exchange.
Dispersion and Arterial Input Functions
BASIL includes a number of Vascular Transport Function (Dispersion Kernel) models of dispersion. These include modelling the VTF as either a Gamma or Gaussian function. For more information see:
Chappell, M. A., Woolrich, M. W., Kazan, S., Jezzard, P., Payne, S. J., & MacIntosh, B. J. (2013). Modeling dispersion in arterial spin labeling: validation using dynamic angiographic measurements. Magnetic Resonance in Medicine, 69(2), 563–570. http://doi.org/10.1002/mrm.24260
Hrabe, J., & Lewis, D. (2004). Two analytical solutions for a model of pulsed arterial spin labeling with randomized blood arrival times. Journal of Magnetic Resonance, 167(1), 49–55.
Exchange and residue functions
As well as the single well-mixed tissue compartment model in which the residue function just accounts for T1 decay (and a small venous outflow), BASIL includes two-compartment exchange models as described in the following papers:
Parkes, L., & Tofts, P. (2002). Improved accuracy of human cerebral blood perfusion measurements using arterial spin labeling: Accounting for capillary water permeability. Magnetic Resonance in Medicine, 48(1), 27–41.
St Lawrence, K., Frank, J., & McLaughlin, A. (2000). Effect of restricted water exchange on cerebral blood flow values calculated with arterial spin tagging: A theoretical investigation. Magnetic Resonance in Medicine, 44(3), 440–449.