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Oxford_ASL

Overview

Oxford_ASL is an automated command line utility that can process ASL data to produce a calibrated map of resting state tissue perfusion. It also includes a range of other useful analysis methods inlcuding amongst others:

  • motion correction
  • registration to a structural image (and thereby a template space)
  • partial volume correction
  • distorition correction

If you have ASL data to analyse, oxford_asl is most likely the tool you will want to use, unless you want a graphical user interface. In practice, the GUI in BASIL is largely a means to construct the right call to oxford_asl.

What you will need

As a minimum to use oxford_asl all you need are some ASL data (label and control pairs). In practice you will also most probably want:

  • a calibration image: normally a proton-density-weighted image (or a close match) acquired with the same readout parameters as the main ASL data. Only once you have a calibration image can you get perfusion in absolute units.
  • a structural image: it is helpful to have a structral image to pass to oxford_asl and if your data incldues this we strongly suggest you do use it with oxford_asl. By preference, we strongly suggest you process your structural image with fsl_anat before passing those results to oxford_asl. This is a good way to get all of the useful information that oxford_asl can use, and you can scrutinise this analysis first to check you are happy with it before starting your ASL analysis.
  • multi-delay ASL: the methods in oxford_asl are perfectly applicable to the widely used single delay/PLD ASL acquisition. But, they offer particular advantages if you have multi-delay/PLD data.

Things to note

To produce the most robust analysis possible oxford_asl includes a number of things in the overall analysis pipeline that you might want to be aware of:

  • spatial regularisation: this feature is now enabled by default for all analyses and applies to the estimated perfusion image. We do not recommend smoothing your data prior to passing to oxford_asl. If you really want to, only do 'sub-voxel' level of smoothing.
  • masking: oxford_asl will attempt to produce a brain mask in which perfusion quantification will be performed. This is normally derived from any structural images with which it is provided (highly recommened), via registration. Therefore, if the registration is poor there will be an impact on the quality of the mask. Where no structural information is provided, the mask will be derived from the ASL data via brain extraction, this can be somewhat variable depending upon your data. It is thus always worth examining the mask created. oxford_asl provides the option to input your own mask where you are not satisfied with the one automatically generated or you need a specific mask for your study.
  • registration: oxford_asl performs the final registration using the perfusion image and the BBR cost function. We have found this to be reliable, as long as the perfusion image is of sufficient quality. In practice, an initial registration is done earlier in the pipeline using the raw ASL images and this is used in the mask generation step. You should always inspect the quality of the final registered images.
  • multiple repeats: ASL data typically contains many repeats of the same measurement to increase the overall signal-to-noise ratio of the data. You should provide this data to oxford_asl, and not average over all the repeats beforehand (unlike earlier versions of the tool). oxford_asl now inlcudes a pipeline where it intially analyses the data having done averaging over the repeats, followed by a subsequent analysis with all the data - to achieve both good robustness and accuracy. If your data has already had the repeats averaged, it is still perfectly reasonable to do analysis with oxford_asl, if you have very few measurements in the data to pass to oxford_asl you might want to use the special 'noise prior' option, since this sets information needed for spatial regularisation.
  • Avanced analyses: Partial volume correction, or analysis of the data into separate epochs, are avaialbe as advanecd supplementary analyses in oxford_asl. If you choose these options oxford_asl will always run a conventional analysis first, this is used to intialise the subsequent analyses. This also means that you can get both conventional and advanced results in a single run of oxford_asl.
  • Multi-stage analysis: By default oxford_asl will analyse the data in multiple-stages where appropriate in an attempt to get as accurate and robust a result as possible. The main example of this is a preliminary analysis with the data having been averaged over multiple-repeats (see above). But, this also applies to the registration (see above). This does mean that you might find some differences in the results than if you did an analysis of the data yourself using a combination of other command line tools.

User Guide

.. toctree::
   :maxdepth: 1

   oxford_asl_userguide