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.idea/.gitignore

+# Default ignored files
+/shelf/
+/workspace.xml
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
+# Editor-based HTTP Client requests
+/httpRequests/

.idea/inspectionProfiles/profiles_settings.xml

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.idea/py_submit.iml

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README.md

+# Multimodal template construction
+
+## Setup - Create a new or clone an existing python 3.7 Conda environment
+
+Make sure the latest versions of the following packages are installed. Installation can be performed with:
+
+```bash
+conda create -n tempy_env python=3.7 pandas numpy
+conda activate tempy_env
+conda install -c conda-forge nibabel
+conda install -c conda-forge fslpy
+pip install file-tree file-tree-fsl
+```
+
+The MMORF singularity package has to be downloaded and the path set:
+```bash
+export MMORFDIR=/path/to/mmorf.sif
+```
+
+Job submission to a computing cluster is currently based on FSL's `fsl_sub` command line tool, 
+which assumes that the computing cluster is managed by SGE.
+
+## Running the template construction pipeline
+Clone this repository: 
+```bash
+git clone https://git.fmrib.ox.ac.uk/cart/ms_templates.git
+cd ms_templates
+
+python run_template_construction.py [Options]
+```
+
+### Options
+####   `-h, --help`
+Show help message
+
+####   `-i <dir>, --input <dir>`
+Directory containing the defaced subjects/timepoints
+
+####   `-t <path>, --tree <path>`
+Path to FSL Filetree describing the subject-specific directory structure and selection of modalities.
+
+The input file structure of the data (i.e. can be a timepoint or a subject) has to be defined in the FSL filetree located in `./tree/data.tree`.
+To construct a combined T1, T2 and DTI template the locations and filenames of e.g. whole-head, brain-extracted and brain masks with respect to 
+the timepoint's directory have to be specified. In the template construction code these will be accessed through Filetree keys, 
+which must not be altered or removed.
+ 
+Required keys for T1: T1_head, T1_brain, T1_brain_mask  
+Required keys for T1+T2: T1_head, T1_brain, T1_brain_mask, T2_head, T2_brain  
+Required keys for T1+T2+DTI: T1_head, T1_brain, T1_brain_mask, T2_head, T2_brain, DTI_scalar, DTI_tensor  
+
+For example to construct a combined T1, T2 and DTI template `./tree/data.tree` would look like:    
+
+```bash
+{sub_id}
+    anat
+        T1
+            T1.nii.gz (T1_head)  
+            T1_brain.nii.gz (T1_brain)  
+            T1_brain_mask.nii.gz (T1_brain_mask)
+        T2
+            T2.nii.gz (T2_head)
+            T2_brain.nii.gz (T2_brain)
+        DTI
+            dti_S0.nii.gz (DTI_scalar)
+            dti_tensor.nii.gz (DTI_tensor)
+```
+
+For a T1-only template this would look like:
+```bash
+{sub_id}
+    anat
+        T1
+            T1.nii.gz (T1_head)  
+            T1_brain.nii.gz (T1_brain)  
+            T1_brain_mask.nii.gz (T1_brain_mask)
+```
+
+`{sub_id}` is a placeholder for the timepoints directory and will be automatically replaced with:
+- IDs in the CSV file provided with `--subids <path>`
+- if the `--subids <path>` flag is not provided, the sub-directories of `--input <dir>` will be used
+
+####   `-o <dir>, --output <dir>`
+Output directory
+
+####   `-s <path>, --subids <path>`
+Path to .csv file containing one subject ID per row: subject IDs have to indentify the sub-directories of the 
+'input' argument (optional)
+
+If not provided all sub-directories of the --input <dir> argument will be used as subject IDs
+
+####   `-aff [True,False], --affine [True,False]`
+Run affine template construction (required for affine template construction)
+
+####   `-nln [True,False], --nonlinear [True,False]`
+Run nonlinear template construction (required for nonlinear template construction)
+
+####   `-c <string>, --cpuq <string>`
+Name of a cluster queue to submit CPU jobs to (required for affine and nonlinear template construction)
+
+####   `-g <string>, --gpuq <string>`
+Name of a cluster queue to submit GPU jobs to (required for nonlinear template construction)
+
+####   `-nres <int>, --n_resolutions <int>`
+Number of resolution levels (required for nonlinear template construction)
+
+####   `-nit <int>, --n_iterations <int>`
+Number of iterations per resolution level (required for nonlinear template construction)
+
+### Example
+A typical command for within-subject template construction given multiple timepoints per subject would lool like this:
+```bash
+python run_template_construction.py 
+        -i /full/path/to/subject/containing/timepoint/directories/
+        -t ./tree/MS_template.tree 
+        -o /full/path/to/output/directory/ 
+        -aff True 
+        -nln True 
+        -nres 6 
+        -nit 1 
+        --cpuq short.qc 
+        --gpuq gpu8.q
+```
+
+
+
+
+

tree/MS_template.tree

+ext_nii=.nii.gz
+ext_mat=.mat
+ext_ini=.ini
+ext_txt=.txt
+
+{data_dir}
+    ->data (data)
+{template_dir}
+    misc (misc_dir)
+        ident{ext_mat} (identity_mat)
+    log (log_dir)
+    scripts (script_dir)
+    clamped_T1 (clamped_dir)
+        {sub_id}_T1_clamped{ext_nii} (T1_head_clamped)
+    affine_it1 (affine_it1_dir)
+        {sub_id} (affine_it1_subject_dir)
+            T1_to_{ref_id}{ext_mat} (T1_to_ref_mat)
+            T1_to_{ref_id}{ext_nii} (T1_to_ref_img)
+            T2_to_T1{ext_mat} (T2_to_T1_mat)
+            DTI_to_T2{ext_mat} (DTI_to_T2_mat)
+            T1_to_unbiased{ext_mat} (T1_to_unbiased_mat)
+            T1_to_unbiased{ext_nii} (T1_to_unbiased_img)
+            T2_to_unbiased{ext_mat} (T2_to_unbiased_mat)
+            DTI_to_unbiased{ext_mat} (DTI_to_unbiased_mat)
+        T1_unbiased_affine_template{ext_nii} (T1_unbiased_affine_template)
+        T1_unbiased_affine_template_to_MNI{ext_nii} (T1_unbiased_affine_template_to_MNI_img)
+        T1_unbiased_affine_template_to_MNI{ext_mat} (T1_unbiased_affine_template_to_MNI_mat)
+        unbiasing_matrix_inverse{ext_mat} (T1_unbiasing_affine_matrix)
+    affine_it2 (affine_it2_dir)
+        {sub_id} (affine_it2_subject_dir)
+            T1_to_MNI152{ext_mat} (T1_to_MNI_mat)
+            T1_brain_to_MNI152{ext_nii} (T1_brain_to_MNI_img)
+            T1_head_to_MNI152{ext_nii} (T1_head_to_MNI_img)
+            T1_brain_mask_to_MNI152{ext_nii} (T1_brain_mask_to_MNI_img)
+            T2_FLAIR_to_MNI152{ext_mat} (T2_to_MNI_mat)
+            T2_FLAIR_brain_to_MNI152{ext_nii} (T2_brain_to_MNI_img)
+            T2_FLAIR_head_to_MNI152{ext_nii} (T2_head_to_MNI_img)
+            DTI_to_MNI152{ext_mat} (DTI_to_MNI_mat)
+            DTI_to_MNI152{ext_nii} (DTI_to_MNI_img)
+            DTI_tensor_to_MNI152{ext_nii} (DTI_tensor_to_MNI)
+        affine_template_T1_brain{ext_nii} (T1_brain_affine_template)
+        affine_template_T1_brain_mask{ext_nii} (T1_brain_mask_affine_template)
+        affine_template_T1_brain_mask_weighted{ext_nii} (T1_brain_mask_weighted_affine_template)
+        affine_template_T1_head{ext_nii} (T1_head_affine_template)
+        affine_template_T2_head{ext_nii} (T2_head_affine_template)
+        affine_template_DTI{ext_nii} (DTI_affine_template)
+        affine_template_DTI_tensor{ext_nii} (DTI_tensor_affine_template)
+    nln_step_{step_id} (nln_step_dir)
+        mmorf_params_step{step_id}{ext_ini} (mmorf_params)
+        iteration_{it_id} (nln_step_iteration_dir)
+            {sub_id}_to_template_warp{ext_nii} (mmorf_warp)
+            {sub_id}_to_template_warp_resampled{ext_nii} (mmorf_warp_resampled)
+            {sub_id}_to_template_warp_resampled_unbiased{ext_nii} (mmorf_warp_resampled_unbiased)
+            {sub_id}_to_template_warp_resampled_unbiased_full_T1_brain{ext_nii} (mmorf_warp_resampled_unbiased_full_T1brain)
+            {sub_id}_to_template_warp_resampled_unbiased_full_T1_head{ext_nii} (mmorf_warp_resampled_unbiased_full_T1head)
+            {sub_id}_to_template_warp_resampled_unbiased_full_T2{ext_nii} (mmorf_warp_resampled_unbiased_full_T2)
+            {sub_id}_to_template_warp_resampled_unbiased_full_DTI{ext_nii} (mmorf_warp_resampled_unbiased_full_DTI)
+            {sub_id}_to_template_jac{ext_nii} (mmorf_jac)
+            {sub_id}_to_template_bias (mmorf_bias)
+            average_warp{ext_nii} (avg_warp)
+            average_invwarp{ext_nii} (inv_avg_warp)
+            {sub_id}_to_template_T1_brain{ext_nii} (warped_T1brain)
+            {sub_id}_to_template_T1_head{ext_nii} (warped_T1head)
+            {sub_id}_to_template_T2_head{ext_nii} (warped_T2head)
+            {sub_id}_to_template_DTI_scalar{ext_nii} (warped_DTIscalar)
+            {sub_id}_to_template_DTI_tensor{ext_nii} (warped_DTItensor)
+            {sub_id}_to_template_T1_brain_mask{ext_nii} (warped_T1brain_mask)
+            nln_template_T1_brain_{step_id}_{it_id}{ext_nii} (T1_brain_nln_template)
+            nln_template_T1_brain_mask_{step_id}_{it_id}{ext_nii} (T1_brain_mask_nln_template)
+            nln_template_T1_brain_mask_weighted_{step_id}_{it_id}{ext_nii} (T1_brain_mask_weighted_nln_template)
+            nln_template_T1_brain_SD_{step_id}_{it_id}{ext_nii} (T1_brain_SD_nln_template)
+            nln_template_T1_head_{step_id}_{it_id}{ext_nii} (T1_head_nln_template)
+            nln_template_T2_head_{step_id}_{it_id}{ext_nii} (T2_head_nln_template)
+            nln_template_DTI_{step_id}_{it_id}{ext_nii} (DTI_nln_template)
+            nln_template_DTI_tensor_{step_id}_{it_id}{ext_nii} (DTI_tensor_nln_template)
+            nln_RMS_delta_intensity_{step_id}_{it_id}{ext_txt} (delta_intensity_output)
+            nln_RMS_delta_avgwarp_{step_id}_{it_id}{ext_txt} (delta_avgwarp_output)
+            nln_RMS_intensity_sd_{step_id}_{it_id}{ext_txt} (sd_intensity_output)

tree/data.tree

+ext_nii=.nii.gz
+ext_mat=.mat
+
+{sub_id}
+    anat
+        T1_orig{ext_nii} (T1_head)
+        T1_brain{ext_nii} (T1_brain)
+        T1_brain_mask{ext_nii} (T1_brain_mask)