The purpose of this exercise is to perform a very simple analysis of ASL data to produce a perfusion image as an introduction to using ASL, esentially following the procedure outlined in Chapter 1 of the Primer using the BASIL tools in FSL.
NOTE that we reccomend you use FSL v6 AND the latest pre-release of BASIL for these exercises (v3.9.22 or greater).
For this exercise you should using the Single PLD pcASL dataset set. This ASL data was acquired using pcASL labelling with a label duration of 1.8 seconds and a post-labelling delay (PLD) of 1.8 seconds, following the recommendations of the ASL consensus paper.
Get the data
It is always a good idea to take a look at the raw data just to check that nothing has gone horribly wrong in the acquisition and ensure you know exactly what you are dealing with.
View these images in fsleyes:
fsleyes aslcalib.nii.gz -dr 0 1500 asltc.nii.gz -dr 0 1500
Since the data looks okay we can proceed to the calculation of a perfusion-weighted image (PWI). This can be achieved relatively simply by subtracting pairs of label and control iamges within the data and then averaging over all the resulting volumes. This will allow us to check that there is perfusion information in the data - as you might have gathered it is realtively easy for the data to look fine on first inspection, but hard to tell if there is going to be a perfusion image in there.
To perform label control subtraction use
asl_file --data=asltc --ntis=1 --iaf=tc --diff --out=asldiffdata --mean=asldiffdata_mean
Note that we have told
asl_file that there is a
single PLD (using
them TIs) and asked for two different outputs, one will contain all
the different volumes after subtraction has been performed
asldiffdata and the other will be the mean over all the
volumes after subtraction
asldiffdata_mean, i.e., the
Look at both of these in FSLeyes:
fsleyes asldiffdata.nii.gz asldiffdata_mean.nii.gz
The PWI (
asldiffdata_mean) has the expected
distirbution for perfusion in the brain - it looks reassuraingly
like a perfusion image. If you now scroll through all the volumes in
asldiffdata you will be able to observe how noisy ASL
data is, but the perfusion pattern should still be visible in all
the different volumes. It is interesting to also load the
aslcalib file and compare the magnitude of voxels in
that with the PWI - there is approximately a factor of 100
We have checked the raw data and also checked the PWI, thus we can proceed to final quantification of perfusion, inverting the kinetics of of the ASL label delviery and using the calibration image to get values in the units of ml/100g/min.
We can do the full quantification using a single call to oxford_asl (it incorporates the asl_file subtraction process):
oxford_asl -i asltc -o oxasl --wp --casl --iaf=tc --tis=3.6 --bolus=1.8 --slicedt=0.0452 -c aslcalib --tr 4.8
Note that we have specified:
For this analysis we have put
oxford_asl into 'White
Paper' mode (
--wp), this means it uses a kinetic model and defaults for the
parameters that are consistent with the formulate in the ASL Consensus Paper.
To view the final result:
You will find something that looks very similar to the PWI we viewed before, but now the values at every vxoel are in ml/100g/min.
You will also find a PWI saved as
oxasl/native_space/perfusion. This is very similar to the
PWI we calculated using
asl_file, except that the kinetic
model inversion has been applied to it, this is the image
We can do a better job of the analysis of this data, by applying some corrections during analysis, this is the subject of Example 2.