Merge Fix/hotfixes into master

.base/manifest.config

@@ -2,11 +2,11 @@
 manifest {
     author = "Alex Valcourt Caron"
     description = """
-        versaflow : a pipeline for robust, reproducible and adaptive preprocessing 
+        versaFlow : a pipeline for robust, reproducible and adaptive preprocessing 
         and modelisation of the axonal structure in primate brain diffusion MRI data
     """.stripMargin()
     doi = "https://doi.org/10.1101/2021.11.22.469616"
-    homePage = "https://github.com/AlexVCaron/versaflow"
-    name = "versaflow"
+    homePage = "https://github.com/AlexVCaron/versaFlow"
+    name = "versaFlow"
     version = "0.9"
 }

.config/t1_to_b0_registration_affine_config.py

@@ -23,6 +23,10 @@
 # AntsConfiguration(mrHARDIConfigurable) configuration
 # -----------------------------------------------------------------------------
 
+c.AntsConfiguration.coarse_angular_split = 3
+
+c.AntsConfiguration.coarse_linear_split = 3
+
 c.AntsConfiguration.accross_modalities = True
 
 c.AntsConfiguration.dimension = 3

.config/t1_to_template_affine_config.py

@@ -14,6 +14,8 @@
 
 c.AntsRegistration.log_level = 30
 
+c.AntsRegistration.init_with_ants_ai = True
+
 c.AntsRegistration.base_config_file = ""
 
 c.AntsRegistration.init_with_ants_ai = True
@@ -24,6 +26,10 @@
 # AntsConfiguration(mrHARDIConfigurable) configuration
 # -----------------------------------------------------------------------------
 
+c.AntsConfiguration.coarse_angular_split = 3
+
+c.AntsConfiguration.coarse_linear_split = 3
+
 c.AntsConfiguration.accross_modalities = False
 
 c.AntsConfiguration.dimension = 3

.config/t1_to_template_affine_quick_config.py

@@ -21,6 +21,12 @@
 # AntsConfiguration(mrHARDIConfigurable) configuration
 # -----------------------------------------------------------------------------
 
+c.AntsConfiguration.coarse_angular_split = 2
+
+c.AntsConfiguration.coarse_linear_split = 3
+
+c.AntsConfiguration.fine_angular_split = 5
+
 c.AntsConfiguration.accross_modalities = False
 
 c.AntsConfiguration.dimension = 3

README.md

@@ -2,11 +2,9 @@
 [![Docker containers](https://img.shields.io/badge/Docker%20images-dockerhub-blue?style=plaflat&logo=docker&labelColor=2e343b)](https://hub.docker.com/repository/docker/avcaron/versa)
 [![DOI](https://zenodo.org/badge/430873937.svg)](https://zenodo.org/badge/latestdoi/430873937)
 
-**versaFlow and its images are moving here ! This repository is still being deployed, there might be some errors running versaFlow right now, we are actively working on it**
+# versaFlow
 
-# versaflow
-
-versaflow is a diffusion MRI processing pipeline configured to handle high 
+versaFlow is a diffusion MRI processing pipeline configured to handle high 
 angular and spatial resolution data. By default it is configured to process 
 Maccaca Mulatta brain images; profiles to handle other types of primates, as 
 well as the human brain are being developped. The pipeline's pre-processing 
@@ -19,10 +17,9 @@ If you use this tool for your research, **please cite the following**
 
 ```
 Valcourt Caron A., Shmuel A., Hao Z., Descoteaux M.,
-“versaflow : A pipeline tailored for the preprocessing and analysis of 
-Multi-Resolution High Angular diffusion MRI and its application to a 
-variability study of the PRIME-DE database”,
-bioRxiv, 2021.11.22.469616, doi.org/10.1101/2021.11.22.469616.
+“versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing 
+and its application to studying the variability of the PRIME-DE database”,
+Frontiers in Neuroinformatics, 10.3389/fninf.2023.1191200, doi.org/10.3389/fninf.2023.1191200.
 ```
 
 ## Requirements
@@ -40,8 +37,7 @@ and require a Nvidia GPU to execute. For usage on a machine without a Nvidia GPU
 use the images tagged `nogpu`.
 
 - Docker : [Docker Hub](https://hub.docker.com/r/avcaron/versa)
-  - Nvidia GPU  : `docker pull avcaron/versa:latest`
-  - Without GPU : `docker pull avcaron/versa:nogpu`
+  - `docker pull avcaron/versa:latest`
 - Singularity :
   - Singularity images are no longer produced in house. To build your singularity, 
     use a docker tag and the following command : `singularity build <image.sif> docker://avcaron/versa:<tag>`.
@@ -50,8 +46,6 @@ use the images tagged `nogpu`.
     - Nvidia GPU  : `singularity pull oras://mrhardi.azurecr.io/mrHARDI/mrhardi:latest`
     - Without GPU : `singularity pull oras://mrhardi.azurecr.io/mrHARDI/mrhardi:nogpu`
 
-
-
 # Data input format
 
 **\*\* All images must be in the compressed Nifti format (.nii.gz) \*\***

USAGE

@@ -1,10 +1,10 @@
-versaflow
+versaFlow
 ===========
 
 
 Usage
 
-nextflow run [-resume] [-with-singularity <sif> | -with-docker [<docker_image> | avcaron/versa:<latest|nogpu>]] [-w <workdir>] [-profile <profile>] -dsl2 main.nf --data_root <root> [ additional parameters ... ]
+nextflow run [-resume] [-with-singularity <sif> | -with-docker [<docker_image> | avcaron/versa:<latest|v*.*.*>]] [-w <workdir>] [-profile <profile>] -dsl2 main.nf --data_root <root> [ additional parameters ... ]
 
 DESCRIPTION
 

main.nf

@@ -65,7 +65,7 @@ def validate_required_parameters () {
 }
 
 def display_run_info () {
-    log.info "versaflow pipeline"
+    log.info "versaFlow pipeline"
     log.info "==================="
     log.info ""
     log.info "Start time : $workflow.start"

modules/functions.nf

@@ -175,7 +175,7 @@ def separate_b0_from_dwi( data_channel ) {
     return [exclude_missing_datapoints(data_channel, 2, ""), filter_datapoints(data_channel, { it[2] == "" })]
 }
 
-LIBRARY_ROOT_NAME = "versaflow"
+LIBRARY_ROOT_NAME = "versaFlow"
 
 def get_data_path () {
     def current_dir = file("$projectDir")

modules/processes/denoise.nf

@@ -125,6 +125,7 @@ process n4_denoise {
         tuple val(sid), path("${image.simpleName}__n4denoised_metadata.*"), optional: true, emit: metadata
         tuple val(sid), path("${image.simpleName}_n4_bias_field.nii.gz"), emit: bias_field
     script:
+        def before_denoise =""
         def after_denoise = ""
         def args = ""
         if ( anat.empty() ) {
@@ -142,14 +143,25 @@ process n4_denoise {
         }
         after_denoise += "fslmaths n4denoise.nii.gz -thr 0 ${image.simpleName}__n4denoised.nii.gz\n"
 
-        if ( !mask.empty() )
+        if ( !mask.empty() ) {
             args += " --mask $mask"
+            if ( !anat.empty() ) {
+                before_denoise += "antsApplyTransforms -n NearestNeighbor -e 0 -r $anat -t identity -i $mask -o $mask\n"
+                before_denoise += "scil_image_math.py convert $mask $mask -f --data_type uint8\n"
+            }
+            else {
+                before_denoise += "antsApplyTransforms -n NearestNeighbor -e 0 -r $image -t identity -i $mask -o $mask\n"
+                before_denoise += "scil_image_math.py convert $mask $mask -f --data_type uint8\n"
+            }
+        }
 
         """
         export OMP_NUM_THREADS=$task.cpus
         export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=$task.cpus
         export OPENBLAS_NUM_THREADS=1
         export ANTS_RANDOM_SEED=$params.random_seed
+
+        $before_denoise
         mrhardi n4 $args \
             --out n4denoise \
             --config $config
@@ -248,6 +260,7 @@ process prepare_epi_correction {
         tuple val(sid), path("${b0s.simpleName}__${algo}_config.cnf"), optional: true, emit: config
         tuple val(sid), val("${sid}__${algo}_results"), emit: awaited_out_name
         tuple val(sid), path("${b0s.simpleName}__${algo}_metadata.*"), emit: metadata
+        tuple val(sid), path("${sid}__${algo}_metadata.*"), emit: metadata_for_corrected_dwi
         tuple val(sid), path("{${dwi_bval.collect{ it.simpleName }.join(",")},${rev_bval.collect{ it.simpleName }.join(",")}}_topup_indexes_metadata.*"), optional: true, emit : in_metadata_w_epi_correction
     script:
         """
@@ -259,6 +272,7 @@ process prepare_epi_correction {
             --b0-thr ${params.b0_threshold ? params.b0_threshold : "0"} \
             --config $config \
             --verbose
+        cp ${b0s.simpleName}__${algo}_metadata.py ${sid}__${algo}_metadata.py
         """
 }
 

modules/workflows/t1_registration.nf

@@ -260,6 +260,7 @@ workflow t12b0_registration {
         reference = extract_b0.out.b0
         resampling_reference = registration_reference
         template_to_t1_transform = template_to_t1_transform
+        template_to_b0_transform = template_to_b0_transform
 }
 
 workflow t1_to_b0_affine {

nextflow.config

@@ -172,7 +172,7 @@ params.max_safe_csf_pvf_threshold = 0.05
 params.max_safe_gm_pvf_threshold = 0.01
 params.safe_csf_mask_dilation = 1
 params.safe_gm_mask_dilation = 0
-params.atropos_prior_weight = 0.0
+params.atropos_prior_weight = 0.3
 
 // Diffusion modeling
 params.strict_parameters = true

workflows/preprocess.nf

@@ -394,6 +394,23 @@ workflow preprocess_wkf {
                     .map{ [it[0], it[1].find{m -> m.simpleName.contains("_rev")}, "rev__ec_input_rev_metadata"] }
             ).map{ it.flatten() }
 
+
+            apply_transform_epi_rev(
+                ec_input_rev_channel.map{ it[0..1] }
+                    .join(epi_correction_wkf.out.transform_reference)
+                    .join(epi_correction_wkf.out.forward_transform)
+                    .join(epi_correction_wkf.out.reverse_transform)
+                    .map{ it[0..-3] + [it[-2] + it[-1]] }
+                    .map{ it + [["true", "false"], "", ""] },
+                "preprocess",
+                "",
+                "false",
+                "",
+                params.ants_transform_base_config
+            )
+            rev_channel = replace_dwi_file(rev_channel, apply_transform_epi_rev.out.image)
+
+            // Applied estimated susceptibility correction to DWI
             ec2eddy_channel = Channel.empty()
             epi_fieldmap_channel = Channel.empty()
             epi_displacement_field_channel = Channel.empty()
@@ -405,7 +422,7 @@ workflow preprocess_wkf {
                 // Applied estimated susceptibility correction to DWI
                 apply_topup_wkf(
                     ec_input_dwi_channel,
-                    ec_input_rev_channel,
+                    apply_transform_epi_rev.out.image,
                     ec2eddy_channel,
                     ec_input_dwi_meta_channel
                         .join(ec_input_rev_meta_channel)
@@ -426,31 +443,8 @@ workflow preprocess_wkf {
                 epi_displacement_field_channel = epi_correction_wkf.out.field
                 epi_fieldmap_channel = epi_correction_wkf.out.fieldmap
 
-                // Applied estimated susceptibility correction to DWI
-                apply_transform_epi_dwi(
-                    ec_input_dwi_channel.map{ it[0..1] }
-                        .join(epi_correction_wkf.out.transform_reference)
-                        .join(epi_correction_wkf.out.forward_transform)
-                        .map{ it + [it[-1].collect{ "false" }, "", ""] },
-                    "preprocess",
-                    "",
-                    "false",
-                    "",
-                    params.ants_transform_base_config
-                )
-                apply_transform_epi_rev(
-                    ec_input_rev_channel.map{ it[0..1] }
-                        .join(epi_correction_wkf.out.transform_reference)
-                        .join(epi_correction_wkf.out.reverse_transform)
-                        .map{ it + [it[-1].collect{ "false" }, "", ""] },
-                    "preprocess",
-                    "",
-                    "false",
-                    "",
-                    params.ants_transform_base_config
-                )
                 apply_epi_field_wkf(
-                    apply_transform_epi_dwi.out.image,
+                    ec_input_dwi_channel.map{ it[0..1] },
                     apply_transform_epi_rev.out.image,
                     epi_displacement_field_channel,
                     ec_input_dwi_meta_channel,
@@ -873,7 +867,7 @@ workflow preprocess_wkf {
 
         // Register T1 to diffusion space (DWI) with masks and segmentations
         template_resampling_reference = null
-        template_to_t1_transform = null
+        template_to_b0_transform = null
         if ( params.register_t1_to_dwi ) {
             t1_registration_wkf(
                 dwi_channel,
@@ -888,7 +882,7 @@ workflow preprocess_wkf {
             )
 
             template_resampling_reference = t1_registration_wkf.out.resampling_reference
-            template_to_t1_transform = t1_registration_wkf.out.template_to_t1_transform
+            template_to_b0_transform = t1_registration_wkf.out.template_to_b0_transform
 
             t1_channel = t1_registration_wkf.out.t1
             t1_mask_channel = t1_registration_wkf.out.mask
@@ -1026,7 +1020,7 @@ workflow preprocess_wkf {
                 absent_pvf_id_channel.join(t1_channel),
                 absent_pvf_id_channel.join(t1_mask_channel),
                 template_resampling_reference,
-                template_to_t1_transform
+                template_to_b0_transform
             )
 
             pvf_channel = segment_nmt_wkf.out.volume_fractions