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v0.0.1: Initial working version of dtiInit Gear.
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‎Dockerfile‎

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# Create Flywheel Gear that can run dtiInit
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# See https://github.com/vistalab/vistasoft/tree/master/mrDiffusion/dtiInit/standalone for source code
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# Start with the Matlab r2013b runtime container
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FROM vistalab/mcr-v82
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MAINTAINER Michael Perry <lmperry@stanford.edu>
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# Install dependencies
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RUN apt-get update \
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&& apt-get install -y \
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zip \
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gzip \
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python
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# ADD the dtiInit Matlab Stand-Alone (MSA) into the container.
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ADD https://github.com/vistalab/vistasoft/raw/97aa8a83ea1e89a900e4c6597a404d84f7390b12/mrDiffusion/dtiInit/standalone/executables/dtiInit_glnxa64_v82 /usr/local/bin/dtiInit
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# Add bet2 (FSL) to the container
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ADD https://github.com/vistalab/vistasoft/raw/f1e7c57bb01bd281be6a8b93cc162994a1307b86/mrAnatomy/Segment/bet2 /usr/local/bin/
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# Add the MNI_EPI template and JSON schema files to the container
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ADD https://github.com/vistalab/vistasoft/raw/f1e7c57bb01bd281be6a8b93cc162994a1307b86/mrDiffusion/templates/MNI_EPI.nii.gz /templates/
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ADD https://github.com/vistalab/vistasoft/raw/97aa8a83ea1e89a900e4c6597a404d84f7390b12/mrDiffusion/dtiInit/standalone/dtiInitStandAloneJsonSchema.json /templates/
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# Copy the help text to display when no args are passed in.
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COPY help.txt /opt/help.txt
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# Ensure that the executable files are executable
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RUN chmod +x /usr/local/bin/bet2 && chmod +x /usr/local/bin/dtiInit
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# Configure environment variables for bet2
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ENV FSLOUTPUTTYPE NIFTI_GZ
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# Make directory for flywheel spec (v0)
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ENV FLYWHEEL /flywheel/v0
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RUN mkdir -p ${FLYWHEEL}
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# Copy and configure run script and metadata code
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COPY run ${FLYWHEEL}/run
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RUN chmod +x ${FLYWHEEL}/run
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COPY manifest.json ${FLYWHEEL}/manifest.json
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ADD https://raw.githubusercontent.com/scitran/utilities/daf5ebc7dac6dde1941ca2a6588cb6033750e38c/metadata_from_gear_output.py ${FLYWHEEL}/metadata_create.py
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RUN chmod +x ${FLYWHEEL}/metadata_create.py
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# Configure entrypoint
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ENTRYPOINT ["/flywheel/v0/run"]
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‎README.md‎

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# dtiinit
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Vistasoft mrDiffusion preprocessing pipeline
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## scitran-apps/dtiinit
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Build context for a [Flywheel Gear](https://github.com/flywheel-io/gears/tree/master/spec) that can preprocess diffusion data using the Vistasoft [dtiInit](https://github.com/vistalab/vistasoft/tree/master/mrDiffusion/dtiInit) pipeline.
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‎build.sh‎

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#!/bin/bash
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# Builds the gear/container
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# The container can be exported using the export.sh script
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GEAR=scitran-apps/dtiinit
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DIR=$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )
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docker build --no-cache --tag $GEAR $DIR

‎dtiinit.json‎

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{
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"input_dir": "/flywheel/v0/input",
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"output_dir": "/flywheel/v0/output",
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"dwi_file": "",
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"bvec_file": "",
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"bval_file": "",
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"t1_file": "",
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"params":
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{
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"bvalue": "",
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"gradDirsCode": "",
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"clobber": 0,
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"dt6BaseName": "",
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"flipLrApFlag": 0,
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"numBootStrapSamples": 500,
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"fitMethod": "ls",
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"nStep": 50,
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"eddyCorrect": 1,
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"excludeVols": "",
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"bsplineInterpFlag": 0,
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"phaseEncodeDir": "",
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"dwOutMm": [2, 2, 2],
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"rotateBvecsWithRx": 0,
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"rotateBvecsWithCanXform": 0,
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"bvecsFile": "",
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"bvalsFile": "",
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"noiseCalcMethod": "b0",
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"outDir": ""
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}
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}
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‎export.sh‎

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#!/bin/bash
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# Exports the container in the cwd.
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# The container can be exported once it's started with
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gear=dtiinit
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version=0.0.1
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outname=$gear-$version.tar
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container=$gear
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image=scitran-apps/$gear
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# Check if input was passed in.
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if [[ -n $1 ]]; then
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outname=$1
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fi
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docker run --name=$container --entrypoint=/bin/true $image
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docker export -o $outname $container
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docker rm $container

‎help.txt‎

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%
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% Read a JSON object, a JSON file, or a directory containing a json file
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% and run dtiInit inside of a docker container (scitran-apps/dtiinit).
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%
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%
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% INPUTS:
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% json - a JSON string, a JSON file, or a directory containing a json
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% file, in the following format (Note that 'input_dir' and
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% 'output_dir' are the only REQUIRED inputs)
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%
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% OUTPUTS:
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% A docker run produces a zip file containing all of the outputs
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% from the algorithm. The name of the output zip file is:
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% 'dtiInit[date-time].zip'
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%
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%
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% JSON SCHEMA:
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% Below is an example JSON file with the defaults show for 'params'.
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% See dtiInitParams.m for more info regarding params. Note that
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% "input_dir" and "output_dir" are required and must be in the
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% context of the container.
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%
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% {
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% "input_dir": "/input",
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% "output_dir": "/output",
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% "dwi_file": "",
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% "bvec_file": "",
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% "bval_file": "",
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% "t1_file": "",
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% "params":
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% {
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% "bvalue": "",
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% "gradDirsCode": "",
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% "clobber": 0,
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% "dt6BaseName": "",
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% "flipLrApFlag": 0,
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% "numBootStrapSamples": 500,
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% "fitMethod": "ls",
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% "nStep": 50,
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% "eddyCorrect": 1,
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% "excludeVols": "",
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% "bsplineInterpFlag": 0,
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% "phaseEncodeDir": "",
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% "dwOutMm": [2, 2, 2],
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% "rotateBvecsWithRx": 0,
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% "rotateBvecsWithCanXform": 0,
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% "bvecsFile": "",
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% "bvalsFile": "",
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% "noiseCalcMethod": "b0",
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% "outDir": "/output/"
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% }
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% }
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%
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%
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% REQUIRED INPUTS:
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% 'input_dir' and 'output_dir' are the only required inputs.
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%
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%
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% HELP:
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% If 'help', '-h', '--help', or nothing (nargin==0), is passed in
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% this help will be displayed.
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%
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%
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% USAGE:
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% Pass in a JSON file, a JSON text string, or a path to a directory
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% containing a JSON file to the docker container to initiate a
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% dtiInit processing run (see INPUT section for JSON schema):
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%
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% % Using a JSON file
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% docker run --rm -ti -v `pwd`/input:/input -v `pwd`/output:/output scitran-apps/dtiinit /input/<JSON_filename>.json
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%
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% % Using a JSON string
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% docker run --rm -ti -v `pwd`/input:/input -v `pwd`/output:/output scitran-apps/dtiinit '{"input_dir":"/input", "output_dir": "/output"}'
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%
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% % Using a directory (in the container), containing a JSON (.json)
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% docker run --rm -ti -v `pwd`/input:/input -v `pwd`/output:/output scitran-apps/dtiinit /input/
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%
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%
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%

‎manifest.json‎

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{
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"name": "dtiinit",
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"label": "Diffusion Data Initialization Pipeline",
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"description": "dtiInit (DTI Initialization) runs the VISTASOFT/mrDiffusion pre-processing pipeline on raw DWI data. See: http://white.stanford.edu/newlm/index.php/DTI_Preprocessing for more information regarding the pipeline. This dtiInit gear will output motion-corrected diffusion data (nifti, bval, bvecs) aligned to the first b0 image in the acquisition. It will also calculate FA, MD, RD, and AD maps. Tensors, vector RGB, brain mask, white-matter mask, and white-matter probability maps will also be output. All outputs will be included in a zip file.",
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"author": "Michael Perry <lmperry@stanford.edu>",
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"url": "https://github.com/vistalab/vistasoft/wiki",
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"source": "https://github.com/scitran-apps/dtiinit",
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"license": "GPL-2.0",
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"flywheel": "0",
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"version": "0.0.1",
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"config": {
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},
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"inputs": {
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"nifti": {
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"base": "file",
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"type": {
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"enum": [
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"nifti"
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]
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}
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},
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"bvec": {
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"base": "file",
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"type": {
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"enum": [
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"bvec"
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]
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}
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},
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"bval": {
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"base": "file",
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"type": {
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"enum": [
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"bval"
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]
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}
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},
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"json": {
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"base": "file",
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"type": {
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"enum": [
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"json"
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]
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}
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}
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}
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}

‎run‎

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#! /bin/bash
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#
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# Run dtiinit Gear
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# Built to flywheel-v0 spec.
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#
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CONTAINER="[scitran-apps/dtiinit]"
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# Configure the ENV
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export FSLOUTPUTTYPE=NIFTI_GZ
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export LD_LIBRARY_PATH=/opt/mcr/v82/runtime/glnxa64:/opt/mcr/v82/bin/glnxa64:/opt/mcr/v82/sys/os/glnxa64:/opt/mcr/v82/sys/java/jre/glnxa64/jre/lib/amd64/native_threads:/opt/mcr/v82/sys/java/jre/glnxa64/jre/lib/amd64/server:/opt/mcr/v82/sys/java/jre/glnxa64/jre/lib/amd64:$LD_LIBRARY_PATH
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export XAPPLRESDIR=/opt/mcr/v82/X11/app-defaults
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FLYWHEEL_BASE=/flywheel/v0
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OUTPUT_DIR=$FLYWHEEL_BASE/output
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NIFTI_DIR=$FLYWHEEL_BASE/nifti
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BVEC_DIR=$FLYWHEEL_BASE/bvec
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BVAL_DIR=$FLYWHEEL_BASE/bval
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JSON_DIR=$FLYWHEEL_BASE/json
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WORK_DIR=$FLYWHEEL_BASE/input
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echo -e "$CONTAINER Initiated"
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if [[ ! -d "$OUTPUT_DIR" ]]
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then
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echo "$CONTAINER $OUTPUT_DIR not found!"
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exit 1
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fi
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# Do the work
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mkdir $WORK_DIR
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cp $NIFTI_DIR/* $BVEC_DIR/* $BVAL_DIR/* $JSON_DIR/* $WORK_DIR
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/usr/local/bin/dtiInit $WORK_DIR 2>&1 | tee $OUTPUT_DIR/dtiinit-log.txt
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# Get a list of the files in the output directory
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outputs=$(find $OUTPUT_DIR/* -maxdepth 0 -type f)
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# If outputs exist, generate metadata, and exit
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if [[ -z $outputs ]]
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then
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echo "$CONTAINER No results found in output directory... Exiting"
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exit 1
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else
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cd $OUTPUT_DIR
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$FLYWHEEL_BASE/metadata_create.py $OUTPUT_DIR $CONTAINER
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echo -e "$CONTAINER Wrote: `ls`"
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echo -e "$CONTAINER Done!"
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fi
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exit 0
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