IPL Longitudinal pipeline

created by Daniel Garcia-Lorenzo, Nicolas Guizard, Berengere Aubert-Broche and Vladimir S. Fonov

Introduction

This collection of python and perl scripts is intended to serve as automatic image processing pipeline. With the main goal to perform automatic analysis on longitudinally acquired high-resoltion (1x1x1mm) T1w scans of multiple subjects. This work relies on various scripts and tools developed during NIHPD project by Vladimir S. Fonov, Matthew Kitching, Andrew Janke and Larry Baer with later additions by Berengere Aubert-Broche and Daniel Garcia-Lorenzo

References

The results obtained with this pipeline have been published in the following article: B. Aubert-Broche et all 2012 "A New Framework for Analyzing Structural Volume Changes of Longitudinal Brain MRI Data"

Installation

Pipeline uses several well-known minc tools, as well as several custom made ones. It also need scoop python module

Dependencies

Pipeline depends on the minc tools from minc-toolkit package TODO: add description of dependencies

Installing pipeline scripts

TODO

Running test case to make sure all the tools are installed properly

TODO

Running

Anatomical Templates

By default, the ICBM 2009c Nonlinear Symmetric template will be used. The location of this template is /opt/minc/share/icbm152_model_09c

Input File Formats

The pipeline needs a text file describing the input data in the following format:

<subject>,<visit>,<t1w.mnc>[,<t2w.mnc>,[<pdw.mnc>,[<sex>[,<age>]]]]

Where

<subject> unique subject identifier, could be any text, mondatory
<visit> unique identifier, could be any text, mondatory
<t1w.mnc> a path to T1w scan , can be relative to the directory where the script will be running, mondatory
<t2w.mnc> a path to T2w scan, optional
<t2w.mnc> a path to PDw scan, optional
<sex> subject's gender, optional
<age> subject's age at the give visit, modatory if longitudinal regularization (see below) is going to be used, optional otherwise Empty fields are allowed, so for a subject with only T1w scan but with known age following example is acceptable:

    JohnDoe,m00,native/JohnDoe_m00_t1w.mnc,,,Male,58
    JohnDoe,m12,native/JohnDoe_m12_t1w.mnc,,,Male,59
    MaryAnn,m00,native/MaryAnn_m00_t1w,,,Female,67
    MaryAnn,m06,native/MaryAnn_m00_t1w,,,Female,67.5

Command line arguments

Example:

    python -m scoop iplLongitudinalPipeline.py \
    -l subjects.lst -o proc -L -D -S \
    --model-dir=/opt/minc/share/icbm152_model_09c \
    --model-name=mni_icbm152_t1_tal_nlin_sym_09c -r \
    --beast-dir=/opt/minc/share/beast-library-1.1

will execute pipeline using all avaialable cores

Example for submisssion on guillimin (Compute canada cluster) with jobs distributed acreoss <N> cores, on arbitrary number of nodes, with time limit of X hrs:

qsub -o <log_file> -N <job_name> run_pipeline.sh Contents of run_pipeline.sh:

#! /bin/sh
#PBS -l nodes=<N>
#PBS -l pmem=4000m
#PBS -l walltime=<X>:00:00
#PBS -A <insert your team id here>
#PBS -j oe
#PBS -o logfile.log
#PBS -V

# change dir to work directory
if [ ! -z $PBS_O_WORKDIR ];then
cd $PBS_O_WORKDIR
fi

# limit number of threads used by ITK and by openMP programs
export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=1
export OMP_NUM_THREADS=1

# start execution of the pipeline
# scoop should be able to determine how many cores we have available
python -m scoop \
    -l <subjects_data>.lst -o <output> -L -D -S \
    --model-dir=<prefix of model>/icbm152_model_09c \
    --model-name=mni_icbm152_t1_tal_nlin_sym_09c -r \
    --beast-dir=<prefix of beast>/beast-library-1.1

Output Files

The pipeline produces following files:

<subject>/<subject>.pickle
<subject>/<subject>.sge.log
<subject>/qc/qc_stx_t1_<subject>_<visit>.jpg
<subject>/qc/qc_stx_mask_<subject>_<visit>.jpg
<subject>/qc/qc_t1t2_<subject>_<visit>.jpg
<subject>/qc/qc_stx_t1_<subject>_v02.jpg
<subject>/qc/qc_stx_mask_<subject>_v02.jpg
<subject>/qc/qc_t1t2_<subject>_v02.jpg
<subject>/qc/qc_stx_t1_<subject>_v03.jpg
<subject>/qc/qc_stx_mask_<subject>_v03.jpg
<subject>/qc/qc_t1t2_<subject>_v03.jpg
<subject>/qc/qc_stx_t1_<subject>_v04.jpg
<subject>/qc/qc_stx_mask_<subject>_v04.jpg
<subject>/qc/qc_t1t2_<subject>_v04.jpg
<subject>/qc/qc_lin_template_<subject>.jpg
<subject>/qc/qc_stx2_t1_<subject>_v04.jpg
<subject>/qc/qc_stx2_t1_<subject>_<visit>.jpg
<subject>/qc/qc_stx2_t1_<subject>_v03.jpg
<subject>/qc/qc_stx2_t1_<subject>_v02.jpg
<subject>/qc/qc_stx2_mask_<subject>_<visit>.jpg
<subject>/qc/qc_stx2_mask_<subject>_v02.jpg
<subject>/qc/qc_stx2_mask_<subject>_v03.jpg
<subject>/qc/qc_stx2_mask_<subject>_v04.jpg
<subject>/qc/qc_nl_template_<subject>.jpg
<subject>/qc/qc_cls_<subject>_<visit>.jpg
<subject>/qc/qc_lob_<subject>_<visit>.jpg
<subject>/qc/qc_cls_<subject>_v02.jpg
<subject>/qc/qc_lob_<subject>_v02.jpg
<subject>/qc/qc_cls_<subject>_v03.jpg
<subject>/qc/qc_lob_<subject>_v03.jpg
<subject>/qc/qc_cls_<subject>_v04.jpg
<subject>/qc/qc_lob_<subject>_v04.jpg
<subject>/qc/qc_lngcls_<subject>_v04.jpg
<subject>/qc/qc_lngcls_<subject>_<visit>.jpg
<subject>/qc/qc_lngcls_<subject>_v03.jpg
<subject>/qc/qc_lngcls_<subject>_v02.jpg
<subject>/<visit>/clp/clp_<subject>_<visit>_t1.mnc
<subject>/<visit>/clp/clp_<subject>_<visit>_mask.mnc
<subject>/<visit>/clp/clp_<subject>_<visit>_t2.mnc
<subject>/<visit>/clp/clp_<subject>_<visit>_t2t1.xfm
<subject>/<visit>/clp/clp_<subject>_<visit>_pdt1.xfm
<subject>/<visit>/clp/clp_<subject>_<visit>_pd.mnc
<subject>/<visit>/clp2/clp2_<subject>_<visit>_t1.mnc
<subject>/<visit>/clp2/clp2_<subject>_<visit>_mask.mnc
<subject>/<visit>/stx/stx_<subject>_<visit>_t1.xfm
<subject>/<visit>/stx/stx_<subject>_<visit>_t1.mnc
<subject>/<visit>/stx/nsstx_<subject>_<visit>_t1.xfm
<subject>/<visit>/stx/nsstx_<subject>_<visit>_t1.mnc
<subject>/<visit>/stx/stx_<subject>_<visit>_mask.mnc
<subject>/<visit>/stx/nsstx_<subject>_<visit>_mask.mnc
<subject>/<visit>/stx/stx_<subject>_<visit>_t2.xfm
<subject>/<visit>/stx/stx_<subject>_<visit>_t2.mnc
<subject>/<visit>/stx/nsstx_<subject>_<visit>_t2.xfm
<subject>/<visit>/stx/stx_<subject>_<visit>_pd.xfm
<subject>/<visit>/stx/stx_<subject>_<visit>_pd.mnc
<subject>/<visit>/stx/stx_<subject>_<visit>_t2les.mnc
<subject>/<visit>/stx/stx_<subject>_<visit>_masknoles.mnc
<subject>/<visit>/stx/nsstx_<subject>_<visit>_masknoles.mnc
<subject>/<visit>/stx2/stx2_<subject>_<visit>_t1.xfm
<subject>/<visit>/stx2/stx2_<subject>_<visit>_t2.xfm
<subject>/<visit>/stx2/stx2_<subject>_<visit>_pd.xfm
<subject>/<visit>/stx2/stx2_<subject>_<visit>_t1.mnc
<subject>/<visit>/stx2/stx2_<subject>_<visit>_t2.mnc
<subject>/<visit>/stx2/stx2_<subject>_<visit>_pd.mnc
<subject>/<visit>/stx2/stx2_<subject>_<visit>_t2les.mnc
<subject>/<visit>/stx2/stx2_<subject>_<visit>_mask.mnc
<subject>/<visit>/stx2/stx2_<subject>_<visit>_masknoles.mnc
<subject>/<visit>/nl/nl_<subject>_<visit>_grid_0.mnc
<subject>/<visit>/nl/nl_<subject>_<visit>.xfm
<subject>/<visit>/vbm/vbm_imp_csf_<subject>_<visit>.mnc
<subject>/<visit>/vbm/vbm_imp_gm_<subject>_<visit>.mnc
<subject>/<visit>/vbm/vbm_imp_wm_<subject>_<visit>.mnc
<subject>/<visit>/vbm/vbm_jac_<subject>_<visit>.mnc
<subject>/<visit>/cls/cls_<subject>_<visit>.mnc
<subject>/<visit>/cls/clsem_<subject>_<visit>.mnc
<subject>/<visit>/cls/lob_<subject>_<visit>.mnc
<subject>/<visit>/cls/lobem_<subject>_<visit>.mnc
<subject>/<visit>/cls/deep_<subject>_<visit>.mnc
<subject>/<visit>/cls/lngcls_<subject>_<visit>.mnc
<subject>/<visit>/cls/lnglob_<subject>_<visit>.mnc
<subject>/<visit>/vol/vol_<subject>_<visit>.txt
<subject>/<visit>/vol/volem_<subject>_<visit>.txt
<subject>/<visit>/vol/deep_<subject>_<visit>.txt
<subject>/<visit>/vol/lngvol_<subject>_<visit>.txt
<subject>/<visit>/lng/lng_<subject>_<visit>_t1.mnc
<subject>/<visit>/lng/lng_<subject>_<visit>_t1_grid_0.mnc
<subject>/<visit>/lng/lng_<subject>_<visit>_t1.xfm
<subject>/lng_template/lin_template_<subject>_t1.mnc
<subject>/lng_template/lin_template_<subject>_mask.mnc
<subject>/lng_template/lin_template_<subject>_t1.xfm
<subject>/lng_template/nl_template_<subject>_t1.mnc
<subject>/lng_template/nl_template_<subject>_mask.mnc
<subject>/lng_template/nl_<subject>.xfm
<subject>/lng_template/nl_<subject>_grid_0.mnc
<subject>/<subject>.commands
<subject>/<subject>.log

in short , qc directory will contain various jpeg files for quality control stx2* files are going to be files in stereotaxic space co-registered between time-points and lngcls_* - tissue classification with longitudinal restriction, lnglob_* - lobe segmentation also with longitudinal restrictions. lng_template will contain subject-specific non-linear average in stereotaxic space.

Optional variants

Distortion correction

TODO

Brain Extraction (BEaST)

TODO

Inter-modality co-registration

TODO

Performing statistical analysis on the results

TODO

Using glim_image

How-to use glim_image for statistical analysis TODO

Using RMINC

How-to use RMINC TODO

Using pyminc

How-to use pyminc TODO

Making changes to the pipeline

Adding new functionality

It is recommended to familiarize yourself with git version control system, the short guide is available here: http://githowto.com/ , another guide is here: http://gitimmersion.com/ or here: http://stackoverflow.com/questions/315911/git-for-beginners-the-definitive-practical-guide

Editing this document

This document is written using markdown see syntax.

Name		Name	Last commit message	Last commit date
Latest commit History 59 Commits
examples		examples
ipl		ipl
.gitignore		.gitignore
README.md		README.md
iplADD.py		iplADD.py
iplAtlasRegistration.py		iplAtlasRegistration.py
iplClassification.py		iplClassification.py
iplConcat.py		iplConcat.py
iplDBM.py		iplDBM.py
iplGeneral.py		iplGeneral.py
iplGenerateModel.py		iplGenerateModel.py
iplLinearAtlasRegistration.py		iplLinearAtlasRegistration.py
iplLinearLngTemplate.py		iplLinearLngTemplate.py
iplLngClassification.py		iplLngClassification.py
iplLngtemplate.py		iplLngtemplate.py
iplLobeSegmentation.py		iplLobeSegmentation.py
iplLongitudinalPipeline.py		iplLongitudinalPipeline.py
iplPatient.py		iplPatient.py
iplQueryVolumes.py		iplQueryVolumes.py
iplScoopFusionGrading.py		iplScoopFusionGrading.py
iplScoopFusionSegmentation.py		iplScoopFusionSegmentation.py
iplScoopGenerateModel.py		iplScoopGenerateModel.py
iplSkullstripping.py		iplSkullstripping.py
iplT1Preprocessing.py		iplT1Preprocessing.py
iplT2PDPreprocessing.py		iplT2PDPreprocessing.py
iplVBM.py		iplVBM.py
ipl_preprocess_pipeline.py		ipl_preprocess_pipeline.py

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IPL Longitudinal pipeline

Introduction

References

Installation

Dependencies

Installing pipeline scripts

Running test case to make sure all the tools are installed properly

Running

Anatomical Templates

Input File Formats

Command line arguments

Output Files

Optional variants

Distortion correction

Brain Extraction (BEaST)

Inter-modality co-registration

Performing statistical analysis on the results

Using glim_image

Using RMINC

Using pyminc

Making changes to the pipeline

Adding new functionality

Editing this document

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Repository files navigation

IPL Longitudinal pipeline

Introduction

References

Installation

Dependencies

Installing pipeline scripts

Running test case to make sure all the tools are installed properly

Running

Anatomical Templates

Input File Formats

Command line arguments

Output Files

Optional variants

Distortion correction

Brain Extraction (BEaST)

Inter-modality co-registration

Performing statistical analysis on the results

Using glim_image

Using RMINC

Using pyminc

Making changes to the pipeline

Adding new functionality

Editing this document

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