Initial commit

.gitattributes

+bip/data/** filter=lfs diff=lfs merge=lfs -text

.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*/__pycache__/
+*.py[cod]
+*$py.class
+
+.DS_Store
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+.*.py
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# My files
+my_init.sh

README

+-------------------------------------------
+BRAIN IMAGING PIPELINE
+Fidel Alfaro Almagro, WIN-FMRIB
+June, 2022
+-------------------------------------------
+Automated tool
+
+Installation
+------------
+If you want to install everythong that is needed from scratch, run:
+```cd /path-to-lib/install_dir/
+python setup.py install```

bb_pipeline/.gitignore

+.DS_Store

bb_pipeline/bb_pipeline.py

+#!/usr/bin/env python
+
+import os
+import sys
+import nibabel as nib
+from fsl import data
+from fsl import utils
+from fsl import scripts
+from fsl import wrappers
+from shutil import copyfile
+from file_tree import FileTree
+from pipe_tree import Pipeline, In, Out, Ref, Var, update_closure
+#from bb_struct.bb_struct import bb_struct
+from my_wrappers.standard_space_roi import standard_space_roi
+from gradunwarp.core.gradient_unwarp_apply import gradient_unwarp_apply
+
+pipe = Pipeline()
+
+BB_BIN_DIR = os.environ['BB_BIN_DIR']
+FSLDIR     = os.environ['FSLDIR']
+coeff      = BB_BIN_DIR + '/data/bb_GDC_coeff.grad'
+MNI        = FSLDIR + '/data/standard/MNI152_T1_1mm'
+
+with open(BB_BIN_DIR + '/data/subj.txt', 'r') as f:
+    subjects = [x.replace('\n','') for x in f.readlines()]
+
+tree = FileTree.read(BB_BIN_DIR + '/data/FileTree').update(subject=subjects)
+
+log_conf = {'cmd': sys.stdout}
+#Alternative (needs to be moved inside bb_struct, as logs_dir is not accessible here):
+#script_name = os.path.splitext(os.path.basename(__file__))[0]
+#log_conf = {'cmd': logs_dir + "/" + script_name + "_cmd.txt"}
+
+@pipe(submit=dict(logdir=Ref("logs_dir"), jobtime=200))
+def bb_struct(  T1_orig:            In,
+                T1_brain:           Out,
+                logs_dir:           Out,
+                T1:                 Out,
+                T1_orig_ud:         Out,
+                T1_orig_ud_warp:    Out,
+                T1_brain_mask:      Out,
+                T1_orig_defaced:    Out,
+                T1_defacing_mask:   Out,
+                T1_unbiased:        Out,
+                T1_unbiased_brain:  Out,
+                T1_brain_to_MNI:    Out,
+                T1_QC_face_mask_inside_brain_mask: Out,
+                transform_outputs:  Out("T1*_to_*"),
+                fast_outputs:       Out("T1_fast_*"),
+                first_outputs:      Out("T1_first_*"),
+                T1_GDC:             Ref,
+                temp_outputs:       Ref("T1_tmp_*")):
+
+    with update_closure(temp_outputs, transform_outputs, fast_outputs,
+                        first_outputs):
+
+        if [ coeff != '' ] :
+            #Calculate and apply the Gradient Distortion Unwarp
+            gradient_unwarp_apply(WD=T1_GDC, infile=T1_orig, outfile=T1_orig_ud,
+                            owarp=T1_orig_ud_warp,gradcoeff=coeff,
+                            vendor='siemens', nojac=True)
+        else:
+            pipe(copyfile, kwargs={'src': In('A'), 'dst': Out('B')})
+
+        #Calculate where does the brain start in the z dimension and extract the roi
+        head_top=int(round(float(wrappers.robustfov(T1_orig_ud).stdout[0].split()[7])))
+        wrappers.fslmaths(T1_orig_ud).roi(0,-1,0,-1,head_top,170,0,1,).run(T1_tmp_1,
+                                                                   log=log_conf)
+
+        #Run a (Recursive) brain extraction on the roi
+        wrappers.bet(T1_tmp_1, T1_tmp_1_brain, robust=True, log=log_conf)
+
+        #Reduce the FOV of T1_orig_ud by calculating a registration
+        #from T1_tmp_brain to ssref and apply it to T1_orig_ud
+        # TODO: Once this wrapper is ready, merge it with FSL wrappers
+        standard_space_roi(T1_tmp_1_brain, T1, maskNONE=True, ssref = MNI + '_brain',
+                           altinput=T1_orig_ud, d=True, log=log_conf)
+
+        #Generate the actual affine from the orig_ud volume to the cut version
+        #we haveand combine it to have an affine matrix from orig_ud to MNI
+        wrappers.flirt(src=T1, ref=T1_orig_ud, omat=T1_to_T1_orig_ud_mat,
+                 schedule = FSLDIR + '/etc/flirtsch/xyztrans.sch', log=log_conf)
+        wrappers.invxfm(inmat=T1_to_T1_orig_ud_mat, omat=T1_orig_ud_to_T1_mat,
+                        log=log_conf)
+        wrappers.concatxfm(atob=T1_to_T1_orig_ud_mat, btoc=T1_orig_ud_to_std_mat,
+                           atoc=T1_to_MNI_linear_mat, log=log_conf)
+
+        #Non-linear registration to MNI using the previously calculated alignment
+        wrappers.fnirt(src = T1, ref = MNI, aff = T1_to_MNI_linear_mat,
+                       config = BB_BIN_DIR + '/data/bb_fnirt.cnf',
+                       refmask = BB_BIN_DIR + '/data/MNI152_T1_1mm_brain_mask_dil_GD7.nii.gz',
+                       logout = logs_dir + '/bb_T1_to_MNI_fnirt.log',
+                       cout = T1_to_MNI_warp_coef, fout = T1_to_MNI_warp,
+                       jout = T1_to_MNI_warp_jac, iout = T1_tmp_3,
+                       interp = 'spline',log=log_conf)
+
+        #Combine all transforms (Gradient Distortion Unwarp and T1 to MNI) into one
+        if coeff != '' :
+            wrappers.convertwarp(ref=MNI, warp1=T1_orig_ud_warp,
+                                 midmat=T1_orig_ud_to_T1_mat, warp2=T1_to_MNI_warp,
+                                 out=T1_orig_to_MNI_warp,log=log_conf)
+        else:
+            wrappers.convertwarp(ref=MNI, premat=T1_orig_ud_to_T1_mat,
+                                 warp1=T1_orig_ud_warp, out=T1_orig_to_MNI_warp,
+                                 log=log_conf)
+
+        # Apply the previously generated transformation
+        wrappers.applywarp(src=T1_orig, ref=MNI, w=T1_orig_to_MNI_warp,
+                           out=T1_brain_to_MNI, rel=True, interp='spline',
+                           log=log_conf)
+
+        #Create brain mask
+        wrappers.invwarp(ref=T1, warp=T1_to_MNI_warp_coef,
+                         out=T1_to_MNI_warp_coef_inv,
+                         log=log_conf)
+        wrappers.applywarp(src=MNI + '_brain_mask', ref=T1 ,
+                           w=T1_to_MNI_warp_coef_inv, out=T1_brain_mask,
+                           rel=True, interp='trilinear', log=log_conf)
+        wrappers.fslmaths(T1).mul(T1_brain_mask).run(T1_brain,log=log_conf)
+        wrappers.fslmaths(T1_brain_to_MNI).mul(MNI + '_brain_mask').run(T1_brain_to_MNI,
+                                                                   log=log_conf)
+
+        #TODO: Replace this part with proper call to fsl_deface
+        #Defacing T1_orig
+        wrappers.concatxfm(atob=T1_orig_ud_to_T1_mat, btoc=T1_to_MNI_linear_mat,
+                           atoc=T1_tmp_mat, log=log_conf)
+        wrappers.concatxfm(atob=T1_tmp_mat,
+                           btoc=BB_BIN_DIR + '/data/MNI_to_MNI_BigFoV_facemask.mat',
+                           atoc=T1_tmp_mat, log=log_conf)
+        wrappers.invxfm(inmat=T1_tmp_mat, omat=T1_tmp_mat, log=log_conf)
+        wrappers.applyxfm(src=BB_BIN_DIR + '/data/MNI152_T1_1mm_BigFoV_facemask', 
+                          ref=T1_orig, mat=T1_tmp_mat, out=T1_defacing_mask,
+                          log=log_conf)
+        wrappers.fslmaths(T1_defacing_mask).binv().mul(T1_orig).run(T1_orig_defaced,
+                                                                    log=log_conf)
+
+        #Defacing T1
+        wrappers.concatxfm(atob=T1_to_MNI_linear_mat,
+                           btoc = BB_BIN_DIR + '/data/MNI_to_MNI_BigFoV_facemask.mat',
+                           atoc = T1_tmp_mat, log=log_conf)
+        wrappers.invxfm(inmat=T1_tmp_mat, omat=T1_tmp_mat, log=log_conf)
+        wrappers.applyxfm(src=BB_BIN_DIR + '/data/MNI152_T1_1mm_BigFoV_facemask',
+                          ref=T1, mat=T1_tmp_mat, out=T1_defacing_mask,
+                          log=log_conf)
+        wrappers.fslmaths(T1_defacing_mask).binv().mul(T1).run(T1, log=log_conf)
+
+        #Generation of QC value: Number of voxels in which
+        #the defacing mask goes into the brain mask
+        wrappers.fslmaths(T1_brain_mask).thr(0.5).bin().run(T1_tmp_4,
+                                                            log=log_conf)
+        wrappers.fslmaths(T1_defacing_mask).thr(0.5).bin().add(T1_tmp_4).run(T1_tmp_4,
+                                                                   log=log_conf)
+        vals = wrappers.fslstats(T1_tmp_4).V.run(log=log_conf)[0]
+        with open(T1_QC_face_mask_inside_brain_mask, 'w') as f:
+            f.write(str(vals))
+
+        #Run fast
+        wrappers.fast(T1_brain, out = T1_fast_dir + '/T1_brain', b=True,
+                      log=log_conf)
+
+        #Binarize PVE masks
+        wrappers.fslmaths(T1_fast_pve_0).thr(0.5).bin().run(T1_fast_CSF_mask,
+                                                            log=log_conf)
+        wrappers.fslmaths(T1_fast_pve_1).thr(0.5).bin().run(T1_fast_WM_mask,
+                                                            log=log_conf)
+        wrappers.fslmaths(T1_fast_pve_2).thr(0.5).bin().run(T1_fast_GM_mask,
+                                                            log=log_conf)
+
+        #Apply bias field correction to T1
+        wrappers.fslmaths(T1).div(T1_fast_brain_bias).run(T1_unbiased,
+                                                          log=log_conf)
+        wrappers.fslmaths(T1_brain).div(T1_fast_brain_bias).run(T1_unbiased_brain,
+                                                                log=log_conf)
+
+        #Creates a link inside T1_first to T1_unbiased_brain.nii.gz
+        os.symlink(src="../T1_unbiased_brain.nii.gz", dst=T1_first_unbiased_brain)
+
+        #pipe(copyfile, kwargs={'src': In('A'), 'dst': Out('B')})
+
+if __name__ == "__main__":
+    pipe.cli(tree)

bb_struct/.gitignore

+.DS_Store

bb_struct/bb_struct.py

+#!/usr/bin/env python
+
+@pipe(submit=dict(logdir=Ref("logs_dir"), jobtime=200))
+def bb_struct(  T1_orig:            In,
+                T1_brain:           Out,
+                logs_dir:           Out,
+                T1:                 Out,
+                T1_orig_ud:         Out,
+                T1_orig_ud_warp:    Out,
+                T1_brain_mask:      Out,
+                T1_orig_defaced:    Out,
+                T1_defacing_mask:   Out,
+                T1_unbiased:        Out,
+                T1_unbiased_brain:  Out,
+                T1_brain_to_MNI:    Out,
+                T1_QC_face_mask_inside_brain_mask: Out,
+                transform_outputs:  Out("T1*_to_*"),
+                fast_outputs:       Out("T1_fast_*"),
+                first_outputs:      Out("T1_first_*"),
+                T1_GDC:             Ref,
+                temp_outputs:       Ref("T1_tmp_*")):
+
+    with update_closure(temp_outputs, transform_outputs, fast_outputs,
+                        first_outputs):
+
+        if [ coeff != '' ] :
+            #Calculate and apply the Gradient Distortion Unwarp
+            gradient_unwarp_apply(WD=T1_GDC, infile=T1_orig, outfile=T1_orig_ud,
+                            owarp=T1_orig_ud_warp,gradcoeff=coeff,
+                            vendor='siemens', nojac=True)
+        else:
+            pipe(copyfile, kwargs={'src': In('A'), 'dst': Out('B')})
+
+        #Calculate where does the brain start in the z dimension and extract the roi
+        head_top=int(round(float(wrappers.robustfov(T1_orig_ud).stdout[0].split()[7])))
+        wrappers.fslmaths(T1_orig_ud).roi(0,-1,0,-1,head_top,170,0,1,).run(T1_tmp_1,
+                                                                   log=log_conf)
+
+        #Run a (Recursive) brain extraction on the roi
+        wrappers.bet(T1_tmp_1, T1_tmp_1_brain, robust=True, log=log_conf)
+
+        #Reduce the FOV of T1_orig_ud by calculating a registration
+        #from T1_tmp_brain to ssref and apply it to T1_orig_ud
+        # TODO: Once this wrapper is ready, merge it with FSL wrappers
+        standard_space_roi(T1_tmp_1_brain, T1, maskNONE=True, ssref = MNI + '_brain',
+                           altinput=T1_orig_ud, d=True, log=log_conf)
+
+        #Generate the actual affine from the orig_ud volume to the cut version
+        #we haveand combine it to have an affine matrix from orig_ud to MNI
+        wrappers.flirt(src=T1, ref=T1_orig_ud, omat=T1_to_T1_orig_ud_mat,
+                 schedule = FSLDIR + '/etc/flirtsch/xyztrans.sch', log=log_conf)
+        wrappers.invxfm(inmat=T1_to_T1_orig_ud_mat, omat=T1_orig_ud_to_T1_mat,
+                        log=log_conf)
+        wrappers.concatxfm(atob=T1_to_T1_orig_ud_mat, btoc=T1_orig_ud_to_std_mat,
+                           atoc=T1_to_MNI_linear_mat, log=log_conf)
+
+        #Non-linear registration to MNI using the previously calculated alignment
+        wrappers.fnirt(src = T1, ref = MNI, aff = T1_to_MNI_linear_mat,
+                       config = BB_BIN_DIR + '/data/bb_fnirt.cnf',
+                       refmask = BB_BIN_DIR + '/data/MNI152_T1_1mm_brain_mask_dil_GD7.nii.gz',
+                       logout = logs_dir + '/bb_T1_to_MNI_fnirt.log',
+                       cout = T1_to_MNI_warp_coef, fout = T1_to_MNI_warp,
+                       jout = T1_to_MNI_warp_jac, iout = T1_tmp_3,
+                       interp = 'spline',log=log_conf)
+
+        #Combine all transforms (Gradient Distortion Unwarp and T1 to MNI) into one
+        if coeff != '' :
+            wrappers.convertwarp(ref=MNI, warp1=T1_orig_ud_warp,
+                                 midmat=T1_orig_ud_to_T1_mat, warp2=T1_to_MNI_warp,
+                                 out=T1_orig_to_MNI_warp,log=log_conf)
+        else:
+            wrappers.convertwarp(ref=MNI, premat=T1_orig_ud_to_T1_mat,
+                                 warp1=T1_orig_ud_warp, out=T1_orig_to_MNI_warp,
+                                 log=log_conf)
+
+        # Apply the previously generated transformation
+        wrappers.applywarp(src=T1_orig, ref=MNI, w=T1_orig_to_MNI_warp,
+                           out=T1_brain_to_MNI, rel=True, interp='spline',
+                           log=log_conf)
+
+        #Create brain mask
+        wrappers.invwarp(ref=T1, warp=T1_to_MNI_warp_coef,
+                         out=T1_to_MNI_warp_coef_inv,
+                         log=log_conf)
+        wrappers.applywarp(src=MNI + '_brain_mask', ref=T1 ,
+                           w=T1_to_MNI_warp_coef_inv, out=T1_brain_mask,
+                           rel=True, interp='trilinear', log=log_conf)
+        wrappers.fslmaths(T1).mul(T1_brain_mask).run(T1_brain,log=log_conf)
+        wrappers.fslmaths(T1_brain_to_MNI).mul(MNI + '_brain_mask').run(T1_brain_to_MNI,
+                                                                   log=log_conf)
+
+        #TODO: Replace this part with proper call to fsl_deface
+        #Defacing T1_orig
+        wrappers.concatxfm(atob=T1_orig_ud_to_T1_mat, btoc=T1_to_MNI_linear_mat,
+                           atoc=T1_tmp_mat, log=log_conf)
+        wrappers.concatxfm(atob=T1_tmp_mat,
+                           btoc=BB_BIN_DIR + '/data/MNI_to_MNI_BigFoV_facemask.mat',
+                           atoc=T1_tmp_mat, log=log_conf)
+        wrappers.invxfm(inmat=T1_tmp_mat, omat=T1_tmp_mat, log=log_conf)
+        wrappers.applyxfm(src=BB_BIN_DIR + '/data/MNI152_T1_1mm_BigFoV_facemask',
+                          ref=T1_orig, mat=T1_tmp_mat, out=T1_defacing_mask,
+                          log=log_conf)
+        wrappers.fslmaths(T1_defacing_mask).binv().mul(T1_orig).run(T1_orig_defaced,
+                                                                    log=log_conf)
+
+        #Defacing T1
+        wrappers.concatxfm(atob=T1_to_MNI_linear_mat,
+                           btoc = BB_BIN_DIR + '/data/MNI_to_MNI_BigFoV_facemask.mat',
+                           atoc = T1_tmp_mat, log=log_conf)
+        wrappers.invxfm(inmat=T1_tmp_mat, omat=T1_tmp_mat, log=log_conf)
+        wrappers.applyxfm(src=BB_BIN_DIR + '/data/MNI152_T1_1mm_BigFoV_facemask',
+                          ref=T1, mat=T1_tmp_mat, out=T1_defacing_mask,
+                          log=log_conf)
+        wrappers.fslmaths(T1_defacing_mask).binv().mul(T1).run(T1, log=log_conf)
+
+        #Generation of QC value: Number of voxels in which
+        #the defacing mask goes into the brain mask
+        wrappers.fslmaths(T1_brain_mask).thr(0.5).bin().run(T1_tmp_4,
+                                                            log=log_conf)
+        wrappers.fslmaths(T1_defacing_mask).thr(0.5).bin().add(T1_tmp_4).run(T1_tmp_4,
+                                                                   log=log_conf)
+        vals = wrappers.fslstats(T1_tmp_4).V.run(log=log_conf)[0]
+        with open(T1_QC_face_mask_inside_brain_mask, 'w') as f:
+            f.write(str(vals))
+
+        #Run fast
+        wrappers.fast(T1_brain, out = T1_fast_dir + '/T1_brain', b=True,
+                      log=log_conf)
+
+        #Binarize PVE masks
+        wrappers.fslmaths(T1_fast_pve_0).thr(0.5).bin().run(T1_fast_CSF_mask,
+                                                            log=log_conf)
+        wrappers.fslmaths(T1_fast_pve_1).thr(0.5).bin().run(T1_fast_WM_mask,
+                                                            log=log_conf)
+        wrappers.fslmaths(T1_fast_pve_2).thr(0.5).bin().run(T1_fast_GM_mask,
+                                                            log=log_conf)
+
+        #Apply bias field correction to T1
+        wrappers.fslmaths(T1).div(T1_fast_brain_bias).run(T1_unbiased,
+                                                          log=log_conf)
+        wrappers.fslmaths(T1_brain).div(T1_fast_brain_bias).run(T1_unbiased_brain,
+                                                                log=log_conf)
+
+        #Creates a link inside T1_first to T1_unbiased_brain.nii.gz
+        os.symlink(src="../T1_unbiased_brain.nii.gz", dst=T1_first_unbiased_brain)
+
+        #pipe(copyfile, kwargs={'src': In('A'), 'dst': Out('B')})

bb_tools/.gitignore

+.DS_Store

bb_tools/correct_file_org.sh

+for elem in `cat subj.txt ` ; do
+  cd $elem/DICOM ;
+  mv T2.dcm T2_FLAIR.dcm ;
+  mv T2.txt T2_FLAIR.txt ;
+  mv T2_strings.txt T2_FLAIR_strings.txt ;
+  cd ../.. ;
+done
+
+for elem in `cat subj.txt ` ; do
+  cd $elem/raw ;
+  if [ -f SWI_TOTAL_MAG_notNorm.nii.gz ] ; then
+    mv SWI_TOTAL_MAG_notNorm.nii.gz SWI_TOTAL_MAG_notNorm_TE1.nii.gz
+  fi
+  if [ -f SWI_TOTAL_MAG_notNorm.json ] ; then
+    mv SWI_TOTAL_MAG_notNorm.json SWI_TOTAL_MAG_notNorm_TE1.json
+  fi
+  cd ../.. ;
+done
+
+for elem in `cat subj.txt ` ; do
+  cd $elem/
+  mv fMRI rfMRI;
+  mkdir tfMRI;
+  if [ -f rfMRI/tfMRI.nii.gz ] ; then
+    mv rfMRI/tfMRI.nii.gz tfMRI/
+  fi
+  if [ -f rfMRI/tfMRI.json ] ; then
+    mv rfMRI/tfMRI.json tfMRI/
+  fi
+  if [ -f rfMRI/tfMRI_SBREF.nii.gz ] ; then
+    mv rfMRI/tfMRI_SBREF.nii.gz tfMRI/
+  fi
+  if [ -f rfMRI/tfMRI_SBREF.json ] ; then
+    mv rfMRI/tfMRI_SBREF.json tfMRI/
+  fi
+  cd ../
+done
+
+for elem in `cat subj.txt ` ; do
+  cd $elem/SWI ;
+  if [ -f SWI_TOTAL_MAG.nii.gz ] ; then
+    mv SWI_TOTAL_MAG.nii.gz SWI_TOTAL_MAG_TE1.nii.gz
+  fi
+  if [ -f SWI_TOTAL_MAG.json ] ; then
+    mv SWI_TOTAL_MAG.json SWI_TOTAL_MAG_TE1.json
+  fi
+  if [ -f SWI_TOTAL_PHA.nii.gz ] ; then
+    mv SWI_TOTAL_PHA.nii.gz SWI_TOTAL_PHA_TE1.nii.gz
+  fi
+  if [ -f SWI_TOTAL_PHA.json ] ; then
+    mv SWI_TOTAL_PHA.json SWI_TOTAL_PHA_TE1.json
+  fi
+  cd ../.. ;
+done
+
+for elem in `cat subj.txt` ; do
+   cd $elem/T1 ;
+   if [ -f T1.nii.gz ] ; then
+      mv T1.nii.gz T1_orig.nii.gz
+   fi
+   if [ -f T1.json ] ; then
+      mv T1.json T1_orig.json
+   fi
+   cd ../..;
+done
+
+for elem in `cat subj.txt` ; do
+   cd $elem/T2_FLAIR ;
+   if [ -f T2_FLAIR.nii.gz ] ; then
+      mv T2_FLAIR.nii.gz T2_FLAIR_orig.nii.gz
+   fi
+   if [ -f T2_FLAIR.json ] ; then
+      mv T2_FLAIR.json T2_FLAIR_orig.json
+   fi
+   cd ../..;
+done
+
+for elem in `cat subj.txt ` ; do
+  cd $elem/SWI ;
+  if [ -f SWI_TOTAL_MAG_TE1.nii.gz ] ; then
+    mv SWI_TOTAL_MAG_TE1.nii.gz SWI_TOTAL_MAG_TE1_orig.nii.gz
+  fi
+  if [ -f SWI_TOTAL_MAG_TE1.json ] ; then
+    mv SWI_TOTAL_MAG_TE1.json SWI_TOTAL_MAG_TE1_orig.json
+  fi
+  if [ -f SWI_TOTAL_MAG_TE2.nii.gz ] ; then
+    mv SWI_TOTAL_MAG_TE2.nii.gz SWI_TOTAL_MAG_TE2_orig.nii.gz
+  fi
+  if [ -f SWI_TOTAL_MAG_TE2.json ] ; then
+    mv SWI_TOTAL_MAG_TE2.json SWI_TOTAL_MAG_TE2_orig.json
+  fi
+  if [ -f SWI_TOTAL_PHA_TE1.nii.gz ] ; then
+    mv SWI_TOTAL_PHA_TE1.nii.gz SWI_TOTAL_PHA_TE1_orig.nii.gz
+  fi
+  if [ -f SWI_TOTAL_PHA_TE1.json ] ; then
+    mv SWI_TOTAL_PHA_TE1.json SWI_TOTAL_PHA_TE1_orig.json
+  fi
+  if [ -f SWI_TOTAL_PHA_TE2.nii.gz ] ; then
+    mv SWI_TOTAL_PHA_TE2.nii.gz SWI_TOTAL_PHA_TE2_orig.nii.gz
+  fi
+  if [ -f SWI_TOTAL_PHA_TE2.json ] ; then
+    mv SWI_TOTAL_PHA_TE2.json SWI_TOTAL_PHA_TE2_orig.json
+  fi
+  cd ../.. ;
+done

data/.gitignore

+.DS_Store
+bb_GDC_*.grad
+subj.txt

data/FileTree.tree

+subject = dummy
+tissue_idx = 0,1,2
+
+{subject}
+   logs (logs_dir)
+   T1 (T1_dir)
+      T1_orig.nii.gz (T1_orig)
+      T1_GDC (T1_GDC)
+      T1.nii.gz (T1)
+      T1_orig_ud.nii.gz (T1_orig_ud)
+      T1_orig_ud_warp.nii.gz (T1_orig_ud_warp)
+      T1_brain.nii.gz (T1_brain)
+      T1_brain_mask.nii.gz (T1_brain_mask)
+      T1_orig_defaced.nii.gz (T1_orig_defaced)
+      T1_defacing_mask.nii.gz (T1_defacing_mask)
+      T1_unbiased.nii.gz (T1_unbiased)
+      T1_unbiased_brain.nii.gz (T1_unbiased_brain)
+      T1_unbiased_mask.nii.gz (T1_unbiased_mask)
+      T1_brain_to_MNI.nii.gz (T1_brain_to_MNI)
+      T1_QC_face_mask_inside_brain_mask.txt (T1_QC_face_mask_inside_brain_mask)
+      T1_tmp_1.nii.gz (T1_tmp_1)
+      T1_tmp_2.nii.gz (T1_tmp_2)
+      T1_tmp_3.nii.gz (T1_tmp_3)
+      T1_tmp_4.nii.gz (T1_tmp_4)
+      T1_tmp.mat (T1_tmp_mat)
+      T1_tmp_1_brain.nii.gz (T1_tmp_1_brain)
+      T1_tmp_to_std.mat (T1_tmp_to_std_mat)
+      T1_tmp_to_std.mat (T1_orig_ud_to_std_mat)
+      transforms (T1_transforms_dir)
+         T1_to_T1_orig_ud.mat (T1_to_T1_orig_ud_mat)
+         T1_orig_ud_to_T1.mat (T1_orig_ud_to_T1_mat)
+         T1_to_MNI_linear.mat (T1_to_MNI_linear_mat)
+         T1_to_MNI_warp_coef.nii.gz (T1_to_MNI_warp_coef)
+         T1_to_MNI_warp.nii.gz (T1_to_MNI_warp)
+         T1_to_MNI_warp_jac.nii.gz (T1_to_MNI_warp_jac)
+         T1_orig_to_MNI_warp.nii.gz (T1_orig_to_MNI_warp)
+         T1_to_MNI_warp_coef_inv.nii.gz (T1_to_MNI_warp_coef_inv)
+      T1_fast (T1_fast_dir)
+         T1_brain_pveseg.nii.gz (T1_fast_pveseg)
+         T1_brain_pve_0.nii.gz (T1_fast_pve_0)
+         T1_brain_pve_1.nii.gz (T1_fast_pve_1)
+         T1_brain_pve_2.nii.gz (T1_fast_pve_2)
+         T1_brain_CSF_mask.nii.gz (T1_fast_CSF_mask)
+         T1_brain_WM_mask.nii.gz (T1_fast_WM_mask)
+         T1_brain_GM_mask.nii.gz (T1_fast_GM_mask)
+         T1_brain_bias.nii.gz (T1_fast_brain_bias)
+      T1_first (T1_first_dir)
+         T1_unbiased_brain.nii.gz (T1_first_unbiased_brain)
+   T2_FLAIR (T2_FLAIR_dir)
+      T2_FLAIR_orig.nii.gz (T2_FLAIR_orig)
+   dMRI (dMRI_dir)
+      raw (dMRI_raw_dir)
+         AP.nii.gz (AP)
+         PA.nii.gz (PA)
+   rfMRI (rfMRI_dir)
+      rfMRI.nii.gz (rfMRI)
+      rfMRI_SBREF.nii.gz (rfMRI_SBREF)
+   tfMRI (tfMRI_dir)
+      tfMRI.nii.gz (tfMRI)
+      tfMRI_SBREF.nii.gz (tfMRI_SBREF)
+   SWI (SWI_dir)
+      SWI_TOTAL_MAG_TE1_orig.nii.gz (SWI_MAG_TE1_orig)
+      SWI_TOTAL_MAG_TE2_orig.nii.gz (SWI_MAG_TE2_orig)
+      SWI_TOTAL_PHA_TE1_orig.nii.gz (SWI_PHA_TE1_orig)
+      SWI_TOTAL_PHA_TE2_orig.nii.gz (SWI_PHA_TE2_orig)
+      MAG_TE1
+         SWI_3MM_UPDATED_V1.1_COILH9_ECHO1_{COIL}.nii.gz (SWI_MAG_TE1_COILS)
+      MAG_TE2
+         SWI_3MM_UPDATED_V1.1_COILH9_ECHO2_{COIL}.nii.gz (SWI_MAG_TE2_COILS)
+      PHA_TE1
+         SWI_3MM_UPDATED_V1.1_COILH10_ECHO1_{COIL}_PH.nii.gz (SWI_PHA_TE1_COILS)
+      PHA_TE2
+         SWI_3MM_UPDATED_V1.1_COILH10_ECHO2_{COIL}_PH.nii.gz (SWI_PHA_TE2_COILS)
+   ASL (ASL_dir)
+      raw (ASL_raw_dir)
+         ASL_M0.nii.gz (ASL_M0)
+         ASL_M0_NORM.nii.gz (ASL_M0_NORM)
+         ASL_PLD_0400_control.nii.gz (ASL_PLD_0400_control)
+         ASL_PLD_0400_label.nii.gz (ASL_PLD_0400_label)
+         ASL_PLD_0800_control.nii.gz (ASL_PLD_0800_control)
+         ASL_PLD_0800_label.nii.gz (ASL_PLD_0800_label)
+         ASL_PLD_1200_control.nii.gz (ASL_PLD_1200_control)
+         ASL_PLD_1200_label.nii.gz (ASL_PLD_1200_label)
+         ASL_PLD_1600_control.nii.gz (ASL_PLD_1600_control)
+         ASL_PLD_1600_label.nii.gz (ASL_PLD_1600_label)
+         ASL_PLD_2000_control.nii.gz (ASL_PLD_2000_control)
+         ASL_PLD_2000_label.nii.gz (ASL_PLD_2000_label)
+   DICOM (DICOM_dir)
+      T1.txt (T1_DICOM_HEADER)
+      T2_FLAIR.txt (T2_FLAIR_DICOM_HEADER)
+      dMRI.txt (dMRI_DICOM_HEADER)
+      rfMRI.txt (rfMRI_DICOM_HEADER)
+      tfMRI.txt (tfMRI_DICOM_HEADER)
+      SWI.txt (SWI_DICOM_HEADER)
+      ASL.txt (ASL_DICOM_HEADER)
+      T1_strings.txt (T1_DICOM_STRINGS)
+      T2_FLAIR_strings.txt (T2_FLAIR_DICOM_STRINGS)
+      dMRI_strings.txt (dMRI_DICOM_STRINGS)
+      rfMRI_strings.txt (rfMRI_DICOM_STRINGS)
+      tfMRI_strings.txt (tfMRI_DICOM_STRINGS)
+      SWI_strings.txt (SWI_DICOM_STRINGS)
+      ASL_strings.txt (ASL_DICOM_STRINGS)
+   raw (raw_dir)
+      T1_notNorm.nii.gz (T1_NOTNORM)
+      T2_FLAIR_notNorm.nii.gz (T2_FLAIR_NOTNORM)
+      AP_SBREF.nii.gz (AP_SBREF)
+      PA_SBREF.nii.gz (PA_SBREF)
+      SWI_TOTAL_MAG_notNorm_TE1.nii.gz (SWI_MAG_TE1_NOTNORM)
+      SWI_TOTAL_MAG_notNorm_TE2.nii.gz (SWI_MAG_TE2_NOTNORM)

data/MNI_to_MNI_BigFoV_facemask.mat

+1 0 0 50
+0 1 0 50
+0 0 1 100
+0 0 0 1

data/bb_fnirt.cnf

+# 	name of reference image
+# --ref=/usr/local/fsl/data/standard/MNI152_T1_2mm.nii.gz
+--ref=MNI152_T1_2mm
+#	name of file with mask in reference space
+# --refmask=/usr/local/fsl/data/standard/MNI152_T1_2mm_brain_mask_dil.nii.gz
+--refmask=MNI152_T1_2mm_brain_mask_dil
+#	If =1, use implicit masking based on value in --ref image. Default =1
+--imprefm=1
+#	If =1, use implicit masking based on value in --in image, Default =1
+--impinm=1
+#	Value to mask out in --ref image. Default =0.0
+--imprefval=0
+#	Value to mask out in --in image. Default =0.0
+--impinval=0
+#	sub-sampling scheme, default 4,2,1,1
+--subsamp=4,4,2,2,1,1
+# 	Max # of non-linear iterations, default 5,5,5,5
+--miter=5,5,5,5,5,10
+#	FWHM (in mm) of gaussian smoothing kernel for input volume, default 6,4,2,2
+--infwhm=8,6,5,4.5,3,2
+#	FWHM (in mm) of gaussian smoothing kernel for ref volume, default 4,2,0,0
+--reffwhm=8,6,5,4,2,0
+#	Weigth of membrane energy regularisation, default depending on --ssqlambda and --regmod switches. See user documetation.
+--lambda=300,150,100,50,40,30
+#	Estimate intensity-mapping if set, deafult 1 (true)
+--estint=1,1,1,1,1,0
+#       Apply the mask if set, default 1 (true)
+--applyrefmask=1,1,1,1,1,1
+#       Apply the mask if set, default 1 (true)
+--applyinmask=1
+#	(approximate) resolution (in mm) of warp basis in x-, y- and z-direction, default 10,10,10
+--warpres=10,10,10
+#	If set (=1), lambda is weighted by current ssq, default 1
+--ssqlambda=1
+#	Model for regularisation of warp-field [membrane_energy bending_energy], default bending_energy
+--regmod=bending_energy
+#	Model for intensity-mapping [none global_linear global_non_linear local_linear global_non_linear_with_bias local_non_linear]
+--intmod=global_non_linear_with_bias
+#	Order of poynomial for mapping intensities, default 5
+--intorder=5
+#	Resolution (in mm) of bias-field modelling local intensities, default 50,50,50
+--biasres=50,50,50
+#	Weight of regularisation for bias-field, default 10000
+--biaslambda=10000
+#	If =1, ref image is used to calculate derivatives. Default =0
+--refderiv=0

data/orig_FileTree.txt

+{subject}
+  T1
+    T1.nii.gz (T1)
+    T1.json
+  T2_FLAIR
+    T2_FLAIR.nii.gz (T2)
+    T2_FLAIR.json
+  dMRI
+    raw
+      AP.nii.gz (AP)
+      AP.bval
+      AP.bvec
+      AP.json
+      PA.nii.gz (PA)
+      PA.bval
+      PA.bvec
+      PA.json
+  rfMRI
+    rfMRI.nii.gz (rfMRI)
+    rfMRI.json
+    rfMRI_SBREF.nii.gz (rfMRI_SBREF)
+    rfMRI_SBREF.json
+  tfMRI
+    tfMRI.nii.gz (tfMRI)
+    tfMRI.json
+    tfMRI_SBREF.nii.gz (tfMRI_SBREF)
+    tfMRI_SBREF.json
+  SWI
+    SWI_TOTAL_MAG_TE1.nii.gz (SWI_MAG_TE1)
+    SWI_TOTAL_MAG_TE1.json
+    SWI_TOTAL_MAG_TE2.nii.gz (SWI_MAG_TE2)
+    SWI_TOTAL_MAG_TE2.json
+    SWI_TOTAL_PHA_TE1.nii.gz (SWI_PHA_TE1)
+    SWI_TOTAL_PHA_TE1.json
+    SWI_TOTAL_PHA_TE2.nii.gz (SWI_PHA_TE2)
+    SWI_TOTAL_PHA_TE2.json
+    MAG_TE1
+      SWI_3MM_UPDATED_V1.1_COILH9_ECHO1_{COIL_MAG_TE1}.nii.gz (SWI_MAG_TE1_COILS)
+    MAG_TE2
+      SWI_3MM_UPDATED_V1.1_COILH9_ECHO2_{COIL_MAG_TE2}.nii.gz (SWI_MAG_TE2_COILS)
+    PHA_TE1
+      SWI_3MM_UPDATED_V1.1_COILH10_ECHO1_{COIL_PHA_TE1}_PH.nii.gz (SWI_PHA_TE1_COILS)
+    PHA_TE2
+      SWI_3MM_UPDATED_V1.1_COILH10_ECHO2_{COIL_PHA_TE2}_PH.nii.gz (SWI_PHA_TE2_COILS)
+  ASL
+     raw
+       ASL_M0.nii.gz (ASL_M0)
+       ASL_M0.json
+       ASL_M0_NORM.nii.gz (ASL_M0_NORM)
+       ASL_M0_NORM.json
+       ASL_PLD_0400_control.nii.gz (ASL_PLD_0400_control)
+       ASL_PLD_0400_control.json
+       ASL_PLD_0400_label.nii.gz (ASL_PLD_0400_label)
+       ASL_PLD_0400_label.json
+       ASL_PLD_0800_control.nii.gz (ASL_PLD_0800_control)
+       ASL_PLD_0800_control.json
+       ASL_PLD_0800_label.nii.gz (ASL_PLD_0800_label)
+       ASL_PLD_0800_label.json
+       ASL_PLD_1200_control.nii.gz (ASL_PLD_1200_control)
+       ASL_PLD_1200_control.json
+       ASL_PLD_1200_label.nii.gz (ASL_PLD_1200_label)
+       ASL_PLD_1200_label.json
+       ASL_PLD_1600_control.nii.gz (ASL_PLD_1600_control)
+       ASL_PLD_1600_control.json
+       ASL_PLD_1600_label.nii.gz (ASL_PLD_1600_label)
+       ASL_PLD_1600_label.json
+       ASL_PLD_2000_control.nii.gz (ASL_PLD_2000_control)
+       ASL_PLD_2000_control.json
+       ASL_PLD_2000_label.nii.gz (ASL_PLD_2000_label)
+       ASL_PLD_2000_label.json
+  DICOM
+      T1.txt (T1_DICOM_HEADER)
+      T2_FLAIR.txt (T2_FLAIR_DICOM_HEADER)
+      dMRI.txt (dMRI_DICOM_HEADER)
+      rfMRI.txt (rfMRI_DICOM_HEADER)
+      tfMRI.txt (tfMRI_DICOM_HEADER)
+      SWI.txt (SWI_DICOM_HEADER)
+      ASL.txt (ASL_DICOM_HEADER)
+      T1_strings.txt (T1_DICOM_STRINGS)
+      T2_strings.txt (T2_DICOM_STRINGS)
+      dMRI_strings.txt (dMRI_DICOM_STRINGS)
+      rfMRI_strings.txt (rfMRI_DICOM_STRINGS)
+      tfMRI_strings.txt (tfMRI_DICOM_STRINGS)
+      SWI_strings.txt (SWI_DICOM_STRINGS)
+      ASL_strings.txt (ASL_DICOM_STRINGS)
+  raw
+    T1_notNorm.nii.gz (T1_NOTNORM)
+    T1_notNorm.json
+    T2_FLAIR_notNorm.nii.gz (T2_FLAIR_NOTNORM)
+    T2_FLAIR_notNorm.json
+    AP_SBREF.nii.gz (AP_SBREF)
+    AP_SBREF.json
+    PA_SBREF.nii.gz (PA_SBREF)
+    PA_SBREF.json
+    SWI_TOTAL_MAG_notNorm_TE1.nii.gz (SWI_MAG_TE1_NOTNORM)
+    SWI_TOTAL_MAG_notNorm_TE1.json
+    SWI_TOTAL_MAG_notNorm_TE2.nii.gz (SWI_MAG_TE2_NOTNORM)
+    SWI_TOTAL_MAG_notNorm_TE2.json