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QSM Pipeline

This docker and singularity image provides the tgv-qsm algorithm (http://www.neuroimaging.at/pages/qsm.php).

If you use this image, this is the reference to cite describing the QSM algorithm: Langkammer, C; Bredies, K; Poser, BA; Barth, M; Reishofer, G; Fan, AP; Bilgic, B; Fazekas, F; Mainero; C; Ropele, S Fast Quantitative Susceptibility Mapping using 3D EPI and Total Generalized Variation. Neuroimage. 2015 May 1;111:622-30. doi: 10.1016/j.neuroimage.2015.02.041. PubMed

If you are looking for a full QSM pipeline including dicom conversion, QSM solution, image segmentation, atlas building

https://github.com/QSMxT/QSMxT

We recommend running this container in the Neurodesk environment for ease of use: https://neurodesk.github.io/

Using the image in singularity (deprecated)

installing singularity will depend on your operating system, here an exampe for a debian based system

sudo apt-get update && sudo apt-get install -y \
    build-essential \
    uuid-dev \
    libgpgme-dev \
    squashfs-tools \
    libseccomp-dev \
    wget \
    pkg-config \
    git \
    cryptsetup-bin

wget https://golang.org/dl/go1.15.2.linux-amd64.tar.gz

tar -C /usr/local -xzf go1.15.2.linux-amd64.tar.gz

export PATH=$PATH:/usr/local/go/bin

export VERSION=3.6.3 && # adjust this as necessary \
    wget https://github.com/sylabs/singularity/releases/download/v${VERSION}/singularity-${VERSION}.tar.gz && \
    tar -xzf singularity-${VERSION}.tar.gz && \
    cd singularity


./mconfig && \
    make -C ./builddir && \
    sudo make -C ./builddir install

then you can download and run the container:

git clone https://github.com/NeuroDesk/transparent-singularity tgvqsm_1.0.0_20210317
cd tgvqsm_1.0.0_20210317
./run_transparent_singularity.sh tgvqsm_1.0.0_20210317

this will download the image, unpack it and provide a wrapper script for starting tgv_qsm:

The wrapper script can be started using

./tgv_qsm

Or you can open a shell into the container:

 singularity shell tgvqsm_1.0.0_20210317.*

you can also bind a different directory to your image (e.g. bind /data from your host to /data in your singularity image)

singularity shell --bind /data:/data/ tgvqsm_1.0.0_20210317.*

Here is an example for a single echo QSM processing:

dcm2niix -o ./ -f magnitude GR_M_5_QSM_p2_1mmIso_TE20/
dcm2niix -o ./ -f phase GR_P_6_QSM_p2_1mmIso_TE20/

bet2 magnitude.nii magnitude_bet2

tgv_qsm \
  -p phase.nii \
  -m magnitude_bet2_mask.nii.gz \
  -f 2.89 \
  -t 0.02 \
  -s \
  -o qsm

The -s option will scale the phase correctly if the phase dicom values are between -2048 and 2048 (should be default on Siemens VD and VE platforms). On the VB platform the phase is between 0 and 4096, so omit the -s option and scale the phase between -pi and pi:

Using the image in docker

docker pull vnmd/tgvqsm_1.0.0:20210317
sudo docker run -it -v $PWD:/data vnmd/tgvqsm_1.0.0:20210317

cd /data
dcm2niix -o ./ -f magnitude GR_M_5_QSM_p2_1mmIso_TE20/
dcm2niix -o ./ -f phase GR_P_6_QSM_p2_1mmIso_TE20/

bet2 magnitude.nii magnitude_bet2

tgv_qsm -p phase.nii -m magnitude_bet2_mask.nii.gz -f 2.89 -t 0.02 -s -o qsm

Optimizing for your CPU

By default, QSM is compiled with the -O3 -march=x86-64 which should provide a good balance between speed and portability. If you know what CPU you're going to be using you can compile with that instruction set to improve performance (e.g. -march=ivybridge for Intel Ivy Bridge CPUs, -march=native for whatever CPU you're currently on). If you would like maximum portability, you can recompile omitting the -march flag altogether.

Using tgv_qsm in Windows Subsystem for Linux (example: Debian based system)

WSL 1.0 doesn't support singularity or docker containers (but WSL 2.0 will). But it is possible to directly install TGV QSM in a miniconda environment:

sudo apt install wget unzip gcc
wget https://repo.anaconda.com/miniconda/Miniconda2-4.6.14-Linux-x86_64.sh
bash Miniconda2-4.6.14-Linux-x86_64.sh
(install, accept agreement with yes, after install source bash again:)
bash
conda install -c anaconda cython==0.25.2
conda install numpy
conda install pyparsing
(make sure pip is not your system pip, but the one in miniconda: which pip)
pip install scipy==0.17.1 nibabel==2.1.0
wget http://www.neuroimaging.at/media/qsm/TGVQSM-plus.zip
unzip TGVQSM-plus.zip
cd TGVQSM-master-011045626121baa8bfdd6633929974c732ae35e3
python setup.py install
cd test_data
tgv_qsm  -p epi3d_test_phase.nii.gz -m epi3d_test_mask.nii.gz -f 2.89 -t 0.027 -o epi3d_test_QSM

Adding fsl to WSL Ubuntu 18.04

wget -O- http://neuro.debian.net/lists/bionic.us-ca.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
sudo apt-key adv --recv-keys --keyserver hkp://pool.sks-keyservers.net:80 0xA5D32F012649A5A9
sudo apt-get update
sudo apt-get install fsl-5.0-core

add ". /etc/fsl/5.0/fsl.sh" to the end of your .profile file

About

This docker and singularity image bundles the tgv-qsm algorithm with bet2, dcm2niix and provides a complete QSM processing pipeline.

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