Mp2rage v1.4

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 brainlife.io
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,68 +1,86 @@
 [![Abcdspec-compliant](https://img.shields.io/badge/ABCD_Spec-v1.1-green.svg)](https://github.com/brain-life/abcd-spec)
-[![Run on Brainlife.io](https://img.shields.io/badge/Brainlife-bl.app.99-blue.svg)](https://doi.org/10.25663/bl.app.99)
+[![Run on Brainlife.io](https://img.shields.io/badge/Brainlife-brainlife.app.116-blue.svg)](https://doi.org/10.25663/brainlife.app.116)
 
-# app-hcp-acpc-alignment
-This app will align a T1w image to the ACPC plane. More specifically, it will align the T1w volume to the MNI152_T1_1mm template using a 6 DOF alignment via FSL commands. This protocol was adapted from the [HCP Preprocessing Pipeline](https://github.com/Washington-University/HCPpipelines). First, the FOV is cropped using FSL's robustfov command. Next, the FOV matrix is inverted using FSL's convert_xfm command. Then, the cropped image is registered to the MNI152_T1_1mm template provided by FSL using FSL's flirt command. Following this, the transformation matrices are concatenated using FSL's convert_xfm command and the cropped image is aligned to the ACPC plane using FSL's aff2rigid. Finally, the original T1 is resampled to ACPC space using FSL's applywarp command.
+# Align mp2rage data to ACPC Plane (HCP-based) 
 
-### Authors
-- Brad Caron ([email protected])
+This app will align mp2rage images to the ACPC plane (specifically, the MNI152_T1_1mm template) from FSL using a 6 DOF alignment via FSL commands. This protocol was adapted from the HCP Preprocessing Pipeline (https://github.com/Washington-University/HCPpipelines.git). Requires mp2rage images input and outputs an MNI_aligned ('ACPC aligned') mp2rage images. 
 
-### Contributors
-- Soichi Hayashi ([email protected])
-- Franco Pestilli ([email protected])
+### Authors 
+
+- Brad Caron ([email protected]) 
+
+### Contributors 
+
+- Soichi Hayashi ([email protected]
+- Franco Pestilli ([email protected]) 
+- Sophia Vinci-Booher ([email protected])
+
+### Funding 
 
-### Funding
 [![NSF-BCS-1734853](https://img.shields.io/badge/NSF_BCS-1734853-blue.svg)](https://nsf.gov/awardsearch/showAward?AWD_ID=1734853)
 [![NSF-BCS-1636893](https://img.shields.io/badge/NSF_BCS-1636893-blue.svg)](https://nsf.gov/awardsearch/showAward?AWD_ID=1636893)
+[![NSF-ACI-1916518](https://img.shields.io/badge/NSF_ACI-1916518-blue.svg)](https://nsf.gov/awardsearch/showAward?AWD_ID=1916518)
+[![NSF-IIS-1912270](https://img.shields.io/badge/NSF_IIS-1912270-blue.svg)](https://nsf.gov/awardsearch/showAward?AWD_ID=1912270)
+[![NIH-NIBIB-R01EB029272](https://img.shields.io/badge/NIH_NIBIB-R01EB029272-green.svg)](https://grantome.com/grant/NIH/R01-EB029272-01)
+
+### Citations 
 
-### References 
-[Glasser et al. (2013) Neuroimage.](https://doi.org/10.1016/j.neuroimage.2013.04.127)
+Please cite the following articles when publishing papers that used data, code or other resources created by the brainlife.io community. 
+
+1. Matthew F. Glasser, Stamatios N. Sotiropoulos, J. Anthony Wilson, Timothy S. Coalson, Bruce Fischl, Jesper L. Andersson, Junqian Xu, Saad Jbabdi, Matthew Webster, Jonathan R. Polimeni, David C. Van Essen, Mark Jenkinson, The minimal preprocessing pipelines for the Human Connectome Project, NeuroImage, Volume 80, 2013, Pages 105-124, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2013.04.127. (http://www.sciencedirect.com/science/article/pii/S1053811913005053) 
 
 ## Running the App 
 
-### On Brainlife.io
+### On Brainlife.io 
+
+You can submit this App online at [https://doi.org/10.25663/brainlife.app.116](https://doi.org/10.25663/brainlife.app.116) via the 'Execute' tab. 
 
-You can submit this App online at [https://doi.org/10.25663/bl.app.99](https://doi.org/10.25663/bl.app.99) via the "Execute" tab.
+### Running Locally (on your machine) 
 
-### Running Locally (on your machine)
+1. git clone this repo 
 
-1. git clone this repo.
-2. Inside the cloned directory, create `config.json` with something like the following content with paths to your input files.
+2. Inside the cloned directory, create `config.json` with something like the following content with paths to your input files. 
 
-```json
+```json 
 {
-        "t1": "./input/t1/t1.nii.gz"
+    "mag_inv1": "./test/mag.inv1.nii.gz",
+    "mag_inv2": "./test/mag.inv2.nii.gz",
+    "unit1":    "./test/unit1.nii.gz",
+    "phase_inv1":   "./test/phase.inv1.nii.gz",
+    "phase_inv2":   "./test/phase.inv2.nii.gz",
+    "template": "MNI_1mm"
 }
-```
+``` 
 
-### Sample Datasets
+### Sample Datasets 
 
-You can download sample datasets from Brainlife using [Brainlife CLI](https://github.com/brain-life/cli).
+You can download sample datasets from Brainlife using [Brainlife CLI](https://github.com/brain-life/cli). 
 
 ```
-npm install -g brainlife
-bl login
-mkdir input
-bl dataset download 5b96bbbf059cf900271924f2 && mv 5b96bbbf059cf900271924f2 input/t1
-```
+npm install -g brainlife 
+bl login 
+mkdir input 
+bl dataset download 
+``` 
 
+3. Launch the App by executing 'main' 
 
-3. Launch the App by executing `main`
+```bash 
+./main 
+``` 
 
-```bash
-./main
-```
+## Output 
 
-## Output
+The main output of this App is is a MNI_aligned ('ACPC aligned') mp2rage images. 
 
-The main output of this App is an ACPC-aligned t1.nii.gz.
+#### Product.json 
 
-#### Product.json
 The secondary output of this app is `product.json`. This file allows web interfaces, DB and API calls on the results of the processing. 
 
-### Dependencies
+### Dependencies 
 
-This App requires the following libraries when run locally.
+This App requires the following libraries when run locally. 
 
-  - singularity: https://singularity.lbl.gov/
-  - FSL: https://hub.docker.com/r/brainlife/fsl/tags/5.0.9
+- singularity: https://singularity.lbl.gov/quickstart
+- FSL: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation
+- jsonlab: https://github.com/fangq/jsonlab

run.sh

@@ -1,16 +1,26 @@
 #!/bin/bash
 
-## This app will align a T1/T2w image to the ACPC plane (specifically, the MNI152_T1/2_1mm template from
+## This app will align a mp2rage image to the ACPC plane (specifically, the MNI152_T1m template from
 ## FSL using a 6 DOF alignment via FSL commands. This protocol was adapted from the HCP Preprocessing
-## Pipeline (https://github.com/Washington-University/HCPpipelines.git). Requires a T1w image input
+## Pipeline (https://github.com/Washington-University/HCPpipelines.git). Requires a mp2rage image input
 ## and outputs an acpc_aligned T1w image.
 
 set -x
 set -e
 
-input=`jq -r '.input' config.json`
+mag_inv1=`jq -r '.mag_inv1' config.json`
+mag_inv1_json=`jq -r '.mag_inv1_json' config.json`
+mag_inv2=`jq -r '.mag_inv2' config.json`
+mag_inv2_json=`jq -r '.mag_inv2_json' config.json`
+phase_inv1=`jq -r '.phase_inv1' config.json`
+phase_inv1_json=`jq -r '.phase_inv1_json' config.json`
+phase_inv2=`jq -r '.phase_inv2' config.json`
+phase_inv2_json=`jq -r '.phase_inv2_json' config.json`
+unit1=`jq -r '.unit1' config.json`
+unit1_json=`jq -r '.unit1_json' config.json`
 template=`jq -r '.template' config.json`
-type=`jq -r '.type' config.json` #T1 or T2
+resample=`jq -r '.resample' config.json`
+interp=`jq -r '.interp' config.json`
 
 product=""
 
@@ -21,36 +31,58 @@ product=""
 case $template in
 nihpd_asym*)
     space="NIHPD"
-    [ $type == "T1" ] && template=templates/${template}_t1w.nii
-    [ $type == "T2" ] && template=templates/${template}_t2w.nii
+    template=templates/${template}_t1w.nii
     ;;
 *)
     space="MNI152NLin6Asym"
-    [ $type == "T1" ] && template=templates/MNI152_T1_1mm
-    [ $type == "T2" ] && template=templates/MNI152_T2_1mm
+    template=templates/MNI152_T1_1mm
     ;;
 esac
 
-case $type in
-T1)
-    output=output/t1.nii.gz
-    ;;
-T2)
-    output=output/t2.nii.gz
-    ;;
-esac
+# identify the volumes in mp2rage datatype
+echo "identifying mp2rage volumes"
+volumes=""
+potential_volumes="mag_inv1 mag_inv2 phase_inv1 phase_inv2 unit1"
+for i in ${potential_volumes}
+do
+    test_vol=$(eval "echo \$${i}")
+    if [ -f ${test_vol} ]; then
+        volumes=${volumes}" ${i}"
+    fi
+done
 
-robustfov -i $input -m roi2full.mat -r input_robustfov.nii.gz
+# crop data
+robustfov -i ${unit1} -m roi2full.mat -r unit1.cropped.nii.gz
 convert_xfm -omat full2roi.mat -inverse roi2full.mat
-flirt -interp spline -in input_robustfov.nii.gz -ref $template -omat roi2std.mat -out acpc_mni.nii.gz
+if [[ ${resample} == true ]]; then
+    echo "computing flirt with resample"
+    flirt -interp ${interp} -in unit1.cropped.nii.gz -ref $template -omat roi2std.mat -out acpc_mni.nii.gz
+else
+    echo "computing flirt without resample"
+    flirt -interp ${interp} -noresample -in unit1.cropped.nii.gz -ref $template -omat roi2std.mat -out acpc_mni.nii.gz
+fi
 convert_xfm -omat full2std.mat -concat roi2std.mat full2roi.mat
 aff2rigid full2std.mat outputmatrix
 
 mkdir -p output
-applywarp --rel --interp=spline -i $input -r $template --premat=outputmatrix -o $output
+for i in ${volumes}
+do
+    input=$(eval "echo \$${i}")
+    outname=`echo ${i/_/.}`
+    if [[ ${interp} == 'nearestneighbour' ]]; then
+        interp="nn"
+    fi
+    applywarp --rel --interp=${interp} -i $input -r $template --premat=outputmatrix -o ./output/${outname}.nii.gz
+done
+
+for i in ${volumes}
+do
+    vol_json=$(eval "echo \$${i}_json")
+    cp -v ${vol_json} ./output/
+done
 
 # make png
-slicer $output -x 0.5 out_aligncheck.png
+slicer ./output/mag.inv1.nii.gz -x 0.5 out_aligncheck.png
 
 # create product.json
 cat << EOF > product.json