cvasl 0.0.1

A package for analysis of MRI

[](To be made)

cvasl is an open source collaborative python library for analysis of brain MRIs. Many functions relate to arterial spin labeled sequences.

This library supports the ongoing research at University of Amsterdam Medical Center on brain ageing, but is being buit for the entire community of radiology researchers across all university and academic medical centers and beyond.

Program files

The main program in this repository (made of the modules in the cvasl folder) contains functions for analysis of MRIs.

Folders and Notebooks

To look around keep in mind the following distinction on folders:

researcher_interface:

• This folder will be added in the future for a growing series of interactive notebooks that allow researchers to investigate questions about their own MRI data

open_work:

• This folder contains experimental work by core members of the cvasl team (including Dr. Candace Makeda Moore, Dr. Dani Bodor, Dr. Henk Mutsaerts)

harmonization_paper:

• This folder contains experimental work for a forthcoming paper by core members of the cvasl team (including Dr. Candace Makeda Moore, Dr. Dani Bodor, Dr. Henk Mutsaerts, and Mathijs Dijsselhof) which deals with radiological image harmonization.

lab_datasets:

• This folder contains notebooks which show how image datasets were assembled in notebooks.

Please keep in mind that at present you will only be able to scroll images in the notebooks with a browser based approach i.e. run Jupyter in a browser but not in VSCode or another IDE to scroll the brain MRIs.

Data sets

The notebooks are configured to run on various datasets. Contact Dr. Candace Makeda Moore( 📫 [e-mail]) to discuss any questions on data configuration for your datasets. In terms of derived value datasets (which use measurements instead of images) You will need tsv and/or csv file datasets arranged in a particular format as specified in seperated_values_specifications.md

Configuring (to work with your data)

In order to preprocess and/or to train models the code needs to be able to locate the raw data you want it to work with.

There are several ways to specify the location of the following directories:

• bids: Special directory. The rest of the directory layout can be derived from its location.

You can store this information persistently in several locations.

1. In the same directory where you run the script (or the notebook). e.g. ./config.json.
2. In home directory, e.g. ~/.cvasl/config.json.
3. In global directory, e.g. /etc/cvasl/config.json.

However, we highly recommend you use the home directory. This file can have this or similar contents:

{

    "bids": "/mnt/source_data",
    "raw_data": "/mnt/source_data/raw_data",
    "derivatives": "/mnt/source_data/derivates/",
    "explore_asl": "/mnt/source_data/derivates/explore_asl",
    "cvage": "/mnt/source_data/derivates/cvage",
    "cvage_inputs": "/mnt/source_data/derivates/cvage/cvage_inputs",
    "cvage_outputs": "/mnt/source_data/derivates/cvage/cvage_outputs",

}

The file is read as follows: if the file only specifies bids directory, then the derivative missing entries are assumed to be relative to the root in a BIDS compliant format order You don't need to specify all entries. If you do, you can overwrite the ALS-BIDS format order but this is not reccomended.

When working from command line, it may be useful to experiment with different directory layouts or to quickly override the existing options. To do so, you can invoke the program like this:

python -m cvasl -C raw_data:/path/to/data <other options>

The snippet above allows using existing config.json but overrides the location of raw_data directory.

python -m cvasl -C bids:/path/to/data -n <other options>

The snippet above ignores any existing config.json files and sets the BIDS directory to /path/to/data (the rest will be derived from it).

python -m cvasl \
    -C raw_data:/path/to/data \
    -C derivatives:/path/to/derivatives \
    -c /custom/config/location \
    <other options>

The snippet above looks for configuration file in the alternative location: /custom/config/location while overriding the locations of raw_data aand of derivatives directories.

If you aren't sure where the data would be read or stored, you can run:

python -m cvasl <some options> dump_config

The above will print the effective configuration that will be used by the program.

Test data

You can get test data by contacting the cvage team. Please email Dr. Moore at [e-mail]

Getting started

How to get the notebooks running? Assuming the raw data set and metadata is available.

0. Assuming you are using conda for package management:

• Make sure you are in no environment:

  conda deactivate
  

  (optional repeat if you are in the base environment)

  You should be in no environment or the base environment now

Option A: Fastest option: In a base-like environment with mamba installed, you can install all Python packages required, using mamba and the environment.yml file.

If you do not have mamba installed you can follow instructions (here)[https://anaconda.org/conda-forge/mamba]

• The command for Windows/Anaconda/Mamba users can be something like:

  mamba env create -f environment.yml
  

Option B: To work with the most current versions with the possibility for development: Install all Python packages required, using conda and the environment.yml file.

• The command for Windows/Anaconda users can be something like:

  conda env create -f environment.yml
  

  Currently, you will then need to clone the repository to run the cvasl from repo. We will soon have an option to create the entire environment at once from conda.

Option C:
* Linux users can create their own environment by hand (use
  install_dev as in setup).

2. If you want to work with command-line, you can do so in your terminal, but if you would like to run our pre-made notebooks, then you can start them by entering jupyter lab into the terminal

Documentation:

You can look at our online documentation or build it by hand (see the setup file, the workflows, and it may be clear)

Testing

The project doesn't include testing data yet.

Command-Line Interface

You will eventually be be able to preprocess, train and use models, and perform other functions using command-line interface. As of now (April 2023) this module is still being built.

Below is an example of how to look at the help for that in general: python -m cvasl --help

And here is an example for a specific function: python -m cvasl hash_over --help

And here are examples of a working commands (file names can be changed): to hash over files (assuming no config file): python -m cvasl -n -C raw_data:test_data hash_over --extension tsv --output some_ignored_folder

to run a debiasing algorithm over files: python -m cvasl -n -C raw_data:image_data debias_over --preprocessing N4_debias_sitk --output ignrd_flder

All long options have short aliases.

✨Copyright 2023 Netherlands eScience Center and U. Amsterdam Medical Center Licensed under See LICENSE for details.✨