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vistalab/fw-gear-dtiinit-diffusion-maps

Generate diffusion maps (in NIfTI format), including Fractional Anisotropy (FA), Axial Diffusivity (AD), Mean Diffusivity (MD), and Radial Diffusivity (RD). The input to this Gear is a dtiInit archive, generated from either dtiInit, or as part of the AFQ processing pipeline.

Inputs

The input to this Gear is a dtiInit archive, containing a 'dt6.mat' file. This archive is generated from either dtiInit, or as part of the AFQ processing pipeline.

dtiInit_29-Aug-2018_17-34-24.zip

Outputs

Diffusion maps (in NIfTI format), including Fractional Anisotropy (FA), Axial Diffusivity (AD), Mean Diffusivity (MD), and Radial Diffusivity (RD).

fa.nii.gz
md.nii.gz
ad.nii.gz
rd.nii.gz

Example Usage/Testing

To run/test this Gear you will need to:

  1. Download the Flywheel CLI and login to your Flywheel Instance
  2. Build the Docker image
  3. Execute the Gear using the Flywheel CLI

1. Download the Flywheel CLI and login

Visit: Flywheel for instruction on dowloading the CLIhttps://docs.flywheel.io/hc/en-us/articles/360008162214-Installing-the-Command-Line-Interface-CLI-

Once you have that, you can login:

fw login <your_flywheel_api_key>

2. Build the image with Docker

git clone https://github.com/vistalab/fw-gear-dtiinit-diffusion-maps
docker build -t vistalab/dtiinit-diffusion-maps:1.0.0

_Important note: The version (1.0.0 in the example above) should be read from the version key within manifest.json file.

3. Run the Gear locally with the test data

fw gear local --dtiinit_archive ./testdata/dtiInit_29-Aug-2018_17-34-24.zip

Example output:

lmperry@warrior:/Users/lmperry/dtiinit-diffusion-maps:$ fw gear local --dtiinit_archive ./testdata/dtiInit_29-Aug-2018_17-34-24.zip

Checking if vistalab/dtiinit-diffusion-maps:1.0.0 is available locally...
        Found tag locally.
Creating container from vistalab/dtiinit-diffusion-maps:1.0.0 ...
        Created de36f2efba27
Attaching to logs...

------------------------------------------
Setting up environment variables
---
LD_LIBRARY_PATH is .:/opt/mcr/v92/runtime/glnxa64:/opt/mcr/v92/bin/glnxa64:/opt/mcr/v92/sys/os/glnxa64:/opt/mcr/v92/sys/opengl/lib/glnxa64

archive_path =

    '/flywheel/v0/input/dtiinit_archive/dtiInit_29-Aug-2018_17-34-24.zip'

Unpacking archive...
Locating dt6.mat file from archive...
Found /tmp/dtiInit_29-Aug-2018_17-34-24/dti90trilin/dt6.mat!
Generating diffusion maps...
Generating fa map ... Writing /flywheel/v0/output/fa.nii.gz...Done.
Generating md map ... Writing /flywheel/v0/output/md.nii.gz...Done.
Generating ad map ... Writing /flywheel/v0/output/ad.nii.gz...Done.
Generating rd map ... Writing /flywheel/v0/output/rd.nii.gz...Done.
Complete!

Removing container de36f2efba27 ...
        Removed container

Developer's Corner: Compiling the Matlab Executable

This Gear runs a Matlab Executable via the Matlab Compiler Runtime, which is build in the base image. Note that the image uses MCR v92 (Matlabr2017a) - this MCR versions used to maintain Docker image compatibility with the other tools generating the dtiInit_Archives.

If you find that need to make changes to the source code you will need to recompile the executable and re-build the Docker image.

In order to recompile the Matlab executable you can use the provided .m file (you need to use Matlabr2017a for the binary to be compatible with the Docker image we generate here).

The dtiinitDiffusionMaps_build.m file contains all the required instruction to compile the binary, including downloading the source code and setting required paths.

You can run the code from the command line like so:

/<path_to_your_matlabr2017a_binary> -nodesktop -r 'dtiinitDiffusionMaps_build.m'

If you already have your Matlab 2017a terminal open, you can simply run the code:

dtiinitDiffusionMaps_build