ASL DRO is software that can generate digital reference objects for Arterial Spin Labelling (ASL) MRI. It creates synthetic raw ASL data according to set acquisition and data format parameters, based on input ground truth maps for:
Intrinsic MRI parameters: M0, T1, T2, T2*
Tissue segmentation (defined as a single tissue type per voxel)
Synthetic data is generated in Brain Imaging Data Structure format, comprising of a NIFTI image file and accompanying json sidecar containing parameters.
ASLDRO was developed to address the need to test ASL image processing pipelines with data that has a known ground truth. A strong emphasis has been placed on ensuring traceability of the developed code, in particular with respect to testing. The DRO pipelines uses a ‘pipe and filter’ architecture with ‘filters’ performing data processing, which provides a common interface between processing blocks.
ASLDRO can be installed as a module directly from the python package index. Once installed it can simply be run as a command-line tool. For more information how to use a python package in this way please see https://docs.python.org/3/installing/index.html
We recommend using the latest version of Python. ASL DRO supports Python 3.7 and newer.
These distributions will be installed automatically when installing ASL DRO.
nibabel provides read / write access to some common neuroimaging file formats
numpy provides efficient calculations with arrays and matrices
jsonschema provides an implementation of JSON Schema validation for Python
nilearn provides image manipulation tools and statistical learning for neuroimaging data
Use a virtual environment to manage the dependencies for your project, both in development and in production.
What problem does a virtual environment solve? The more Python projects you have, the more likely it is that you need to work with different versions of Python libraries, or even Python itself. Newer versions of libraries for one project can break compatibility in another project.
Virtual environments are independent groups of Python libraries, one for each project. Packages installed for one project will not affect other projects or the operating system’s packages.
Python comes bundled with the
venv module to create virtual
Create an environment
Create a project folder and a
venv folder within:
$ mkdir myproject $ cd myproject $ python3 -m venv venv
$ py -3 -m venv venv
Activate the environment
Before you work on your project, activate the corresponding environment:
$ . venv/bin/activate
Your shell prompt will change to show the name of the activated environment.
Install ASL DRO
Within the activated environment, use the following command to install ASL DRO:
$ pip install asldro
ASL DRO is now installed. Check out the Quickstart or go to the Documentation Overview.
Eager to get started? This page gives a good introduction to ASL DRO. Follow Installation to set up a project and install ASL DRO first.
After installation the command line tool
asldro will be made available. You can run:
asldro generate path/to/output_file.zip
to run the DRO generation as-per the ASL White Paper specification. The output file may be either .zip or .tar.gz.
Is it also possible to specify a parameter file, which will override any of the default values:
asldro generate --params path/to/input_params.json path/to/output_file.zip
It is possible to create an example parameters file containing the model defaults by running:
asldro output params /path/to/input_params.json
which will create the
/path/to/input_params.json file. The parameters may be adjusted as
necessary and used with the ‘generate’ command.
For details on input parameters see Parameters
It is also possible to output the high-resolution ground-truth (HRGT) files. To get a list of the available data, type:
asldro output hrgt -h
To output the HRGT, type:
asldro output hrgt HRGT OUTPUT_DIR
where HRGT is the code of the files to download, and OUTPUT_DIR is the directory to output to.
There are three pipelines available in ASLDRO
The full ASL pipeline.
A structural MRI pipeline (generates gradient echo, spin echo or inversion recovery signal).
A ground truth pipeline that simply resamples the input ground truth to the specified resolution.
In a single instance of ASLDRO, the input parameter file can configure any number and configurations of these pipelines to be run, much in the way that this can be done on an MRI scanner.
The full ASL pipeline comprises of:
Loading in the ground truth volumes.
Producing $\Delta M$ using the General Kinetic Model for the specified ASL parameters.
Generating synthetic M0, Control and Label volumes.
Sampling at the acquisition resolution
Adding instrument and physiological pseudorandom noise.
The structural pipeline excludes the General Kinetic Model, and just generates volumes with synthetic MR contrast. The ground truth pipeline only has the motion model and sampling.
Each volume described in
asl_context has the motion, resampling and noise processes applied
independently. The rotation and translation arrays in the input parameters describe this motion, and
the the random number generator is initialised with the same seed each time the DRO is run, so each
volume will have noise that is unique, but statistically the same.
desired_snr is set to
0, the resultant images will not have any noise applied.
Each pipeline outputs files in BIDS (https://bids.neuroimaging.io/) format, consisting of a NIFTI image file and accompanying json sidecar. In the case of an ASL image an additional ‘*_aslcontext.tsv’ file is also generated which describes the ASL volumes present in the timeseries.
The DRO pipeline is summarised in this schematic (click to view full-size):
Development of this software project must comply with a few code styling/quality rules and processes:
Pylint must be used as the linter for all source code. A linting configuration can be found in
.pylintrc. There should be no linting errors when checking in code.
Before committing any files, black must be run with the default settings in order perform autoformatting on the project.
Before pushing any code, make sure the CHANGELOG.md is updated as per the instructions in the CHANGELOG.md file.
The project’s software development processes must be used (found here).