Merge branch 'master' into correct_oblique

Dockerfile

@@ -18,7 +18,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 # Install a few minimal AFNI components
 RUN curl -L -O https://afni.nimh.nih.gov/pub/dist/tgz/linux_ubuntu_16_64.tgz && \
     mkdir -p /opt/afni && \
-    tar xzvf linux_ubuntu_16_64.tgz -C /opt/afni linux_ubuntu_16_64/{libmri.so,libf2c.so,3dDespike,3dTshift,3dWarp} --strip-components=1 && \
+    tar xzvf linux_ubuntu_16_64.tgz -C /opt/afni linux_ubuntu_16_64/{libmri.so,libf2c.so,3dDespike,3dTshift,3dWarp,3dAutobox} --strip-components=1 && \
     rm -f linux_ubuntu_16_64.tgz
 ENV PATH=/opt/afni${PATH:+:$PATH}
 

rabies/analysis_pkg/analysis_functions.py

@@ -26,7 +26,7 @@ def seed_based_FC(dict_file, seed_dict, seed_name):
     seed_file = seed_dict[seed_name]
     resampled = os.path.abspath('resampled.nii.gz')
     command=f'antsApplyTransforms -i {seed_file} -r {mask_file} -o {resampled} -n GenericLabel'
-    rc = run_command(command)
+    rc,c_out = run_command(command)
     roi_mask = sitk.GetArrayFromImage(sitk.ReadImage(resampled))[volume_indices].astype(bool)
 
     # extract the voxel timeseries within the mask, and take the mean ROI timeseries
@@ -139,7 +139,7 @@ def run_group_ICA(bold_file_list, mask_file, dim, random_seed):
     from rabies.utils import run_command
     out_dir = os.path.abspath('group_melodic.ica')
     command = f'melodic -i {file_path} -m {mask_file} -o {out_dir} -d {dim} --report --seed={str(random_seed)}'
-    rc = run_command(command)
+    rc,c_out = run_command(command)
     IC_file = out_dir+'/melodic_IC.nii.gz'
     return out_dir, IC_file
 

rabies/analysis_pkg/diagnosis_pkg/diagnosis_functions.py

@@ -29,7 +29,7 @@ def resample_mask(in_file, ref_file):
             transform_string += f"-t {transform} "
 
     command = f'antsApplyTransforms -i {in_file} {transform_string}-n GenericLabel -r {ref_file} -o {out_file}'
-    rc = run_command(command)
+    rc,c_out = run_command(command)
     return out_file
 
 

rabies/analysis_pkg/main_wf.py

@@ -393,27 +393,6 @@ def read_confound_workflow(conf_output, nativespace=False):
     return split_dict, split_name, target_list
 
 
-def get_iterable_scan_list(scan_list, split_name):
-    # prep the subset of scans on which the analysis will be run
-    import numpy as np
-    import pandas as pd
-    if os.path.isfile(os.path.abspath(scan_list[0])):
-        updated_split_name=[]
-        if '.nii' in pathlib.Path(scan_list[0]).name:
-            for scan in scan_list:
-                updated_split_name.append(find_split(scan, split_name))
-        else:
-            # read the file as a .txt
-            scan_list = np.array(pd.read_csv(os.path.abspath(scan_list[0]), header=None)).flatten()
-            for scan in scan_list:
-                updated_split_name.append(find_split(scan, split_name))
-    elif scan_list[0]=='all':
-        updated_split_name = split_name
-    else:
-        raise ValueError(f"The --scan_list {scan_list} input had improper format. It must the full path to a .txt or .nii files, or 'all' to keep all scans.")
-    return updated_split_name
-
-
 def find_split(scan, split_name):
     for split in split_name:
         if split in scan:

rabies/parser.py

@@ -774,19 +774,6 @@ def get_parser():
             "path for analysis outputs.\n"
             "\n"
         )
-    analysis.add_argument(
-        '--scan_list', type=str,
-        nargs="*",  # 0 or more values expected => creates a list
-        default=['all'],
-        help=
-            "This option offers to run the analysis on a subset of the scans. The scans are selected by\n"
-            "providing the full path to the corresponding EPI file in the input BIDS folder. The list \n"
-            "of scan can be specified manually as a list of file name '--scan_list scan1.nii.gz \n"
-            "scan2.nii.gz ...' or the files can be imbedded into a .txt file with one filename per row.\n"
-            "By default, 'all' will use all the scans previously processed.\n"
-            "(default: %(default)s)\n"
-            "\n"
-        )
     analysis.add_argument(
         '--prior_maps', action='store', type=Path,
         default=f"{rabies_path}/melodic_IC.nii.gz",

rabies/preprocess_pkg/bold_main_wf.py

@@ -1,5 +1,5 @@
 from nipype.pipeline import engine as pe
-from nipype.interfaces import utility as niu, afni
+from nipype.interfaces import utility as niu
 
 from .hmc import init_bold_hmc_wf,EstimateMotionParams
 from .bold_ref import init_bold_reference_wf
@@ -8,7 +8,7 @@
 from .inho_correction import init_inho_correction_wf
 from .registration import init_cross_modal_reg_wf
 from nipype.interfaces.utility import Function
-
+from .utils import apply_despike
 
 def init_bold_main_wf(opts, output_folder, number_functional_scans, inho_cor_only=False, name='bold_main_wf'):
     """
@@ -193,9 +193,10 @@ def resample_isotropic(bold_file, rabies_data_type):
                 ])
 
         if opts.apply_despiking:
-            despike = pe.Node(
-                afni.Despike(outputtype='NIFTI_GZ'),
-                name='despike')
+            despike = pe.Node(Function(input_names=['in_file'],
+                                                        output_names=['out_file'],
+                                                        function=apply_despike),
+                                            name='despike')
             workflow.connect([
                 (inputnode, despike, [('bold', 'in_file')]),
                 (despike, boldbuffer, [('out_file', 'bold_file')]),

rabies/preprocess_pkg/bold_ref.py

@@ -172,7 +172,7 @@ def _run_interface(self, runtime):
         # denoise the resulting reference image through non-local mean denoising
         # Denoising reference image.
         command = f'DenoiseImage -d 3 -i {out_ref_fname} -o {out_ref_fname}'
-        rc = run_command(command)
+        rc,c_out = run_command(command)
 
         setattr(self, 'ref_image', out_ref_fname)
 

rabies/preprocess_pkg/commonspace_reg.py

@@ -515,7 +515,7 @@ def _run_interface(self, runtime):
         template_folder = self.inputs.output_folder
 
         command = f'mkdir -p {template_folder}'
-        rc = run_command(command)
+        rc,c_out = run_command(command)
 
         merged = flatten_list(list(self.inputs.moving_image_list))
         # create a csv file of the input image list
@@ -580,13 +580,13 @@ def _run_interface(self, runtime):
         # for an intermediate modelbuild step and include --close and --initial-transform to the registration.
         # To avoid this, the template file is renamed to another generic filename. 
         command = f'cp {self.inputs.template_anat} {template_folder}/modelbuild_starting_target.nii.gz'
-        rc = run_command(command)
+        rc,c_out = run_command(command)
         log.debug(f"The --starting-target template original file is {self.inputs.template_anat}, and was renamed to {template_folder}/modelbuild_starting_target.nii.gz.")
 
         command = f'QBATCH_SYSTEM={cluster_type} QBATCH_CORES={num_threads} modelbuild.sh \
             --float --average-type median --gradient-step 0.25 --iterations 2 --starting-target {template_folder}/modelbuild_starting_target.nii.gz --stages rigid,affine,nlin \
             --output-dir {template_folder} --sharpen-type unsharp --block --debug {masks} {csv_path}'
-        rc = run_command(command)
+        rc,c_out = run_command(command)
 
 
         unbiased_template = template_folder + \

rabies/preprocess_pkg/hmc.py

@@ -123,7 +123,7 @@ def _run_interface(self, runtime):
         # change the name of the first iteration directory to prevent overlap of files with second iteration
         if self.inputs.second:
             command = 'mv ants_mc_tmp first_ants_mc_tmp'
-            rc = run_command(command)
+            rc,c_out = run_command(command)
 
         # make a tmp directory to store the files
         os.makedirs('ants_mc_tmp', exist_ok=True)
@@ -212,7 +212,7 @@ def _run_interface(self, runtime):
                 raise ValueError("No smoothing coefficient was found.")
         else:
             raise ValueError("Wrong moreaccurate provided.")
-        rc = run_command(command)
+        rc,c_out = run_command(command)
 
         setattr(self, 'csv_params', os.path.abspath('ants_mc_tmp/motcorrMOCOparams.csv'))
         setattr(self, 'mc_corrected_bold', os.path.abspath('ants_mc_tmp/motcorr.nii.gz'))
@@ -395,14 +395,14 @@ def _run_interface(self, runtime):
         # first the voxelwise positioning map
         command = f'antsMotionCorrStats -m {self.inputs.motcorr_params} -o {filename_split[0]}_pos_file.csv -x {self.inputs.raw_brain_mask} \
                     -d {self.inputs.raw_bold}'
-        rc = run_command(command)
+        rc,c_out = run_command(command)
         pos_voxelwise = os.path.abspath(
             f"{filename_split[0]}_pos_file.nii.gz")
 
         # then the voxelwise framewise displacement map
         command = f'antsMotionCorrStats -m {self.inputs.motcorr_params} -o {filename_split[0]}_FD_file.csv -x {self.inputs.raw_brain_mask} \
                     -d {self.inputs.raw_bold} -f 1'
-        rc = run_command(command)
+        rc,c_out = run_command(command)
 
         FD_csv = os.path.abspath(f"{filename_split[0]}_FD_file.csv")
         FD_voxelwise = os.path.abspath(f"{filename_split[0]}_FD_file.nii.gz")

rabies/preprocess_pkg/inho_correction.py

@@ -277,7 +277,7 @@ def _run_interface(self, runtime):
             else:
                 raise ValueError(f"Image type must be 'EPI' or 'structural', {self.inputs.image_type}")
             command = f'{processing_script} {target_img} {corrected} {self.inputs.anat_ref} {self.inputs.anat_mask} {self.inputs.inho_cor_method} {self.inputs.multistage_otsu} {str(self.inputs.otsu_threshold)}'
-            rc = run_command(command)
+            rc,c_out = run_command(command)
 
             resampled_mask = corrected.split('.nii.gz')[0]+'_mask.nii.gz'
             init_denoise = corrected.split('.nii.gz')[0]+'_init_denoise.nii.gz'
@@ -297,25 +297,25 @@ def _run_interface(self, runtime):
                 input_anat = target_img
 
                 command = 'ImageMath 3 null_mask.nii.gz ThresholdAtMean %s 0' % (input_anat)
-                rc = run_command(command)
+                rc,c_out = run_command(command)
                 command = 'ImageMath 3 thresh_mask.nii.gz ThresholdAtMean %s 1.2' % (input_anat)
-                rc = run_command(command)
+                rc,c_out = run_command(command)
 
                 command = 'N4BiasFieldCorrection -d 3 -s 4 -i %s -b [20] -c [200x200x200,0.0] -w thresh_mask.nii.gz -x null_mask.nii.gz -o N4.nii.gz' % (input_anat)
-                rc = run_command(command)
+                rc,c_out = run_command(command)
                 command = 'DenoiseImage -d 3 -i N4.nii.gz -o denoise.nii.gz'
-                rc = run_command(command)
+                rc,c_out = run_command(command)
 
                 from rabies.preprocess_pkg.registration import run_antsRegistration
                 [affine, warp, inverse_warp, warped_image] = run_antsRegistration(reg_method='Affine', moving_image=input_anat, fixed_image=self.inputs.anat_ref, fixed_mask=self.inputs.anat_mask)
 
                 command = 'antsApplyTransforms -d 3 -i %s -t [%s,1] -r %s -o resampled_mask.nii.gz -n GenericLabel' % (self.inputs.anat_mask, affine, input_anat)
-                rc = run_command(command)
+                rc,c_out = run_command(command)
 
                 command = 'N4BiasFieldCorrection -d 3 -s 2 -i %s -b [20] -c [200x200x200x200,0.0] -w resampled_mask.nii.gz -r 1 -x null_mask.nii.gz -o N4.nii.gz' % (input_anat)
-                rc = run_command(command)
+                rc,c_out = run_command(command)
                 command = 'DenoiseImage -d 3 -i N4.nii.gz -o %s' % (corrected)
-                rc = run_command(command)
+                rc,c_out = run_command(command)
 
                 # resample image to specified data format
                 sitk.WriteImage(sitk.ReadImage(corrected, self.inputs.rabies_data_type), corrected)
@@ -412,7 +412,7 @@ def _run_interface(self, runtime):
         [affine, warp, inverse_warp, warped_image] = run_antsRegistration(reg_method='Rigid', moving_image=cwd+'/corrected_iter2.nii.gz', fixed_image=self.inputs.anat, fixed_mask=self.inputs.anat_mask)
 
         command = 'antsApplyTransforms -d 3 -i %s -t [%s,1] -r %s -o %s -n GenericLabel' % (self.inputs.anat_mask, affine, cwd+'/corrected_iter2.nii.gz',resampled_mask)
-        rc = run_command(command)
+        rc,c_out = run_command(command)
 
         otsu_bias_cor(target=bias_cor_input, otsu_ref=cwd+'/corrected_iter2.nii.gz', out_name=cwd+'/final_otsu.nii.gz', b_value=b_value, mask=resampled_mask)
 
@@ -444,9 +444,9 @@ def otsu_bias_cor(target, otsu_ref, out_name, b_value, mask=None, n_iter=200):
     import SimpleITK as sitk
     from rabies.utils import run_command
     command = 'ImageMath 3 null_mask.nii.gz ThresholdAtMean %s 0' % (otsu_ref)
-    rc = run_command(command)
+    rc,c_out = run_command(command)
     command = 'ThresholdImage 3 %s otsu_weight.nii.gz Otsu 4' % (otsu_ref)
-    rc = run_command(command)
+    rc,c_out = run_command(command)
 
     otsu_img = sitk.ReadImage(
         'otsu_weight.nii.gz', sitk.sitkUInt8)
@@ -485,16 +485,16 @@ def otsu_bias_cor(target, otsu_ref, out_name, b_value, mask=None, n_iter=200):
     sitk.WriteImage(mask_img, 'mask1234.nii.gz')
 
     command = 'N4BiasFieldCorrection -d 3 -i %s -b %s -s 1 -c [%sx%sx%s,1e-4] -w mask12.nii.gz -x null_mask.nii.gz -o corrected1.nii.gz' % (target, str(b_value), str(n_iter),str(n_iter),str(n_iter),)
-    rc = run_command(command)
+    rc,c_out = run_command(command)
 
     command = 'N4BiasFieldCorrection -d 3 -i corrected1.nii.gz -b %s -s 1 -c [%sx%sx%s,1e-4] -w mask34.nii.gz -x null_mask.nii.gz -o corrected2.nii.gz' % (str(b_value), str(n_iter),str(n_iter),str(n_iter),)
-    rc = run_command(command)
+    rc,c_out = run_command(command)
 
     command = 'N4BiasFieldCorrection -d 3 -i corrected2.nii.gz -b %s -s 1 -c [%sx%sx%s,1e-4] -w mask123.nii.gz -x null_mask.nii.gz -o corrected3.nii.gz' % (str(b_value), str(n_iter),str(n_iter),str(n_iter),)
-    rc = run_command(command)
+    rc,c_out = run_command(command)
 
     command = 'N4BiasFieldCorrection -d 3 -i corrected3.nii.gz -b %s -s 1 -c [%sx%sx%s,1e-4] -w mask234.nii.gz -x null_mask.nii.gz -o corrected4.nii.gz' % (str(b_value), str(n_iter),str(n_iter),str(n_iter),)
-    rc = run_command(command)
+    rc,c_out = run_command(command)
 
     command = 'N4BiasFieldCorrection -d 3 -i corrected4.nii.gz -b %s -s 1 -c [%sx%sx%s,1e-4] -w mask1234.nii.gz -x null_mask.nii.gz -o %s' % (str(b_value), str(n_iter),str(n_iter),str(n_iter),out_name,)
-    rc = run_command(command)
+    rc,c_out = run_command(command)

rabies/preprocess_pkg/main_wf.py

@@ -4,11 +4,10 @@
 from nipype.interfaces import utility as niu
 from nipype.interfaces.io import DataSink
 from nipype.interfaces.utility import Function
-from nipype.interfaces.afni import Autobox
 from .inho_correction import init_inho_correction_wf
 from .commonspace_reg import init_commonspace_reg_wf
 from .bold_main_wf import init_bold_main_wf
-from .utils import BIDSDataGraber, prep_bids_iter, convert_to_RAS, convert_oblique2card, resample_template
+from .utils import BIDSDataGraber, prep_bids_iter, convert_to_RAS, convert_oblique2card, apply_autobox, resample_template
 from . import preprocess_visual_QC
 
 def init_main_wf(data_dir_path, output_folder, opts, name='main_wf'):
@@ -195,8 +194,10 @@ def init_main_wf(data_dir_path, output_folder, opts, name='main_wf'):
                                         name="format_bold_buffer")
 
     if opts.bold_autobox: # apply AFNI's 3dAutobox
-        bold_autobox = pe.Node(Autobox(padding=1, outputtype='NIFTI_GZ'),
-                                name="bold_autobox")
+        bold_autobox = pe.Node(Function(input_names=['in_file'],
+                                                    output_names=['out_file'],
+                                                    function=apply_autobox),
+                                        name='bold_autobox')
         workflow.connect([
             (bold_convert_to_RAS_node, bold_autobox, [
                 ('RAS_file', 'in_file'),
@@ -374,8 +375,10 @@ def init_main_wf(data_dir_path, output_folder, opts, name='main_wf'):
                                             name="format_anat_buffer")
 
         if opts.anat_autobox: # apply AFNI's 3dAutobox
-            anat_autobox = pe.Node(Autobox(padding=1, outputtype='NIFTI_GZ'),
-                                    name="anat_autobox")
+            anat_autobox = pe.Node(Function(input_names=['in_file'],
+                                                        output_names=['out_file'],
+                                                        function=apply_autobox),
+                                            name='anat_autobox')
             workflow.connect([
                 (anat_convert_to_RAS_node, anat_autobox, [
                     ('RAS_file', 'in_file'),

rabies/preprocess_pkg/preprocess_visual_QC.py

@@ -51,7 +51,7 @@ def _run_interface(self, runtime):
             filename_template+f'_registration.png'
 
         command = f'{script_path} {self.inputs.moving} {self.inputs.fixed} {out_name}'
-        rc = run_command(command)
+        rc,c_out = run_command(command)
 
         setattr(self, 'out_figure', out_name)
         return runtime

rabies/preprocess_pkg/registration.py

@@ -114,7 +114,7 @@ def run_antsRegistration(reg_method, brain_extraction=False, moving_image='NULL'
     else:
         command = f'{reg_call} {moving_image} {moving_mask} {fixed_image} {fixed_mask} {filename_split[0]}'
     from rabies.utils import run_command
-    rc = run_command(command)
+    rc,c_out = run_command(command)
 
     cwd = os.getcwd()
     warped_image = f'{cwd}/{filename_split[0]}_output_warped_image.nii.gz'

rabies/preprocess_pkg/stc.py

@@ -102,7 +102,7 @@ def slice_timing_correction(in_file, tr='auto', tpattern='alt-z', stc_axis='Y',
 
     command = f'3dTshift -{interp_method} -prefix temp_tshift.nii.gz -tpattern {tpattern} -TR {tr} {target_file}'
     from rabies.utils import run_command
-    rc = run_command(command)
+    rc,c_out = run_command(command)
 
     tshift_img = sitk.ReadImage(
         'temp_tshift.nii.gz', rabies_data_type)

rabies/preprocess_pkg/utils.py

@@ -172,6 +172,29 @@ def _list_outputs(self):
         return {'out_file': getattr(self, 'out_file')}
 
 
+###WRAPPERS FOR AFNI'S FUNCTIONS; NECESSARY TO PREVENT ISSUES WHEN READING INPUTS/OUTPUTS FROM WORKFLOW GRAPH
+
+def apply_despike(in_file):
+    import pathlib
+    import os
+    from rabies.utils import run_command
+    split = pathlib.Path(in_file).name.rsplit(".nii")[0]
+    out_file = os.path.abspath(f"{split}_despike.nii.gz")
+    command = f'3dDespike -prefix {out_file} {in_file}'
+    rc,c_out = run_command(command)
+    return out_file
+
+def apply_autobox(in_file):
+    import pathlib
+    import os
+    from rabies.utils import run_command
+    split = pathlib.Path(in_file).name.rsplit(".nii")[0]
+    out_file = os.path.abspath(f"{split}_autobox.nii.gz")
+    command = f'3dAutobox -input {in_file} -prefix {out_file} -npad 1'
+    rc,c_out = run_command(command)
+    return out_file
+
+
 def convert_to_RAS(img_file, out_dir=None):
     # convert the input image to the RAS orientation convention
     import os
@@ -241,7 +264,7 @@ def resample_template(template_file, mask_file, file_list, spacing='inputs_defin
     # also resample the brain mask to ensure stable registrations further down
     resampled_mask = os.path.abspath("resampled_mask.nii.gz")
     command = f'antsApplyTransforms -d 3 -i {mask_file} -r {resampled_template} -o {resampled_mask} --verbose -n GenericLabel'
-    rc = run_command(command)
+    rc,c_out = run_command(command)
 
     return resampled_template, resampled_mask
 

rabies/run_main.py

@@ -326,7 +326,7 @@ def install_DSURQE(log):
         from rabies.preprocess_pkg.utils import run_command
         log.info(
             "SOME FILES FROM THE DEFAULT TEMPLATE ARE MISSING. THEY WILL BE INSTALLED BEFORE FURTHER PROCESSING.")
-        rc = run_command(f'install_DSURQE.sh {rabies_path}', verbose=True)
+        rc,c_out = run_command(f'install_DSURQE.sh {rabies_path}', verbose=True)
 
 
 def check_binary_masks(mask):