/
main.nf
1921 lines (1625 loc) · 64.9 KB
/
main.nf
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#!/usr/bin/env nextflow
import groovy.json.*
params.input = false
params.fs = false
params.bidsignore = false
params.bids = false
params.bids_config = false
params.help = false
params.dti_shells = false
params.fodf_shells = false
if(params.help) {
usage = file("$baseDir/USAGE")
cpu_count = Runtime.runtime.availableProcessors()
bindings = ["clean_bids":"$params.clean_bids",
"sh_fitting":"$params.sh_fitting",
"sh_fitting_basis":"$params.sh_fitting_basis",
"sh_fitting_order":"$params.sh_fitting_order",
"b0_thr_extract_b0":"$params.b0_thr_extract_b0",
"dwi_shell_tolerance":"$params.dwi_shell_tolerance",
"dilate_b0_mask_prelim_brain_extraction":"$params.dilate_b0_mask_prelim_brain_extraction",
"bet_prelim_f":"$params.bet_prelim_f",
"run_dwi_denoising":"$params.run_dwi_denoising",
"extent":"$params.extent",
"run_gibbs_correction": "$params.run_gibbs_correction",
"run_topup":"$params.run_topup",
"encoding_direction":"$params.encoding_direction",
"readout":"$params.readout",
"run_eddy":"$params.run_eddy",
"eddy_cmd":"$params.eddy_cmd",
"bet_topup_before_eddy_f":"$params.bet_topup_before_eddy_f",
"use_slice_drop_correction":"$params.use_slice_drop_correction",
"bet_dwi_final_f":"$params.bet_dwi_final_f",
"fa_mask_threshold":"$params.fa_mask_threshold",
"run_resample_dwi":"$params.run_resample_dwi",
"dwi_resolution":"$params.dwi_resolution",
"dwi_interpolation":"$params.dwi_interpolation",
"max_dti_shell_value":"$params.max_dti_shell_value",
"min_fodf_shell_value":"$params.min_fodf_shell_value",
"run_t1_denoising":"$params.run_t1_denoising",
"run_resample_t1":"$params.run_resample_t1",
"t1_resolution":"$params.t1_resolution",
"t1_interpolation":"$params.t1_interpolation",
"number_of_tissues":"$params.number_of_tissues",
"fa":"$params.fa",
"min_fa":"$params.min_fa",
"min_nvox":"$params.min_nvox",
"roi_radius":"$params.roi_radius",
"set_frf":"$params.set_frf",
"manual_frf":"$params.manual_frf",
"mean_frf":"$params.mean_frf",
"sh_order":"$params.sh_order",
"basis":"$params.basis",
"fodf_metrics_a_factor":"$params.fodf_metrics_a_factor",
"relative_threshold":"$params.relative_threshold",
"max_fa_in_ventricle":"$params.max_fa_in_ventricle",
"min_md_in_ventricle":"$params.min_md_in_ventricle",
"run_pft_tracking":"$params.run_pft_tracking",
"pft_seeding_mask_type":"$params.pft_seeding_mask_type",
"pft_fa_seeding_mask_threshold":"$params.pft_fa_seeding_mask_threshold",
"pft_algo":"$params.pft_algo",
"pft_seeding":"$params.pft_seeding",
"pft_nbr_seeds":"$params.pft_nbr_seeds",
"pft_step":"$params.pft_step",
"pft_theta":"$params.pft_theta",
"pft_min_len":"$params.pft_min_len",
"pft_max_len":"$params.pft_max_len",
"pft_compress_streamlines":"$params.pft_compress_streamlines",
"pft_compress_value":"$params.pft_compress_value",
"local_seeding_mask_type":"$params.local_seeding_mask_type",
"local_fa_seeding_mask_threshold":"$params.local_fa_seeding_mask_threshold",
"local_tracking_mask_type":"$params.local_tracking_mask_type",
"local_fa_tracking_mask_threshold":"$params.local_fa_tracking_mask_threshold",
"run_local_tracking":"$params.run_local_tracking",
"local_compress_streamlines":"$params.local_compress_streamlines",
"pft_random_seed":"$params.pft_random_seed",
"local_algo":"$params.local_algo",
"local_seeding":"$params.local_seeding",
"local_nbr_seeds":"$params.local_nbr_seeds",
"local_step":"$params.local_step",
"local_theta":"$params.local_theta",
"local_sfthres":"$params.local_sfthres",
"local_sfthres_init":"$params.local_sfthres_init",
"local_min_len":"$params.local_min_len",
"local_max_len":"$params.local_max_len",
"local_compress_value":"$params.local_compress_value",
"local_random_seed":"$params.local_random_seed",
"local_batch_size_gpu":"$params.local_batch_size_gpu",
"local_tracking_gpu":"$params.local_tracking_gpu",
"cpu_count":"$cpu_count",
"template_t1":"$params.template_t1",
"processes_brain_extraction_t1":"$params.processes_brain_extraction_t1",
"processes_denoise_dwi":"$params.processes_denoise_dwi",
"processes_denoise_t1":"$params.processes_denoise_t1",
"processes_eddy":"$params.processes_eddy",
"processes_fodf":"$params.processes_fodf",
"processes_registration":"$params.processes_registration",
"processes_local_tracking":"$params.processes_local_tracking"]
engine = new groovy.text.SimpleTemplateEngine()
template = engine.createTemplate(usage.text).make(bindings)
print template.toString()
return
}
log.info "TractoFlow pipeline"
log.info "==================="
log.info ""
log.info "Start time: $workflow.start"
log.info ""
workflow.onComplete {
log.info "Pipeline completed at: $workflow.complete"
log.info "Execution status: ${ workflow.success ? 'OK' : 'failed' }"
log.info "Execution duration: $workflow.duration"
}
if( (!nextflow.version.matches('>=19.04.2'))) {
error "This workflow requires Nextflow (>=19.04.2, <=21.12.1) -- You are running version $nextflow.version"
}
if( (nextflow.version.matches('>21.12.1.edge'))) {
error "This workflow requires Nextflow (>=19.04.2, <=21.12.1) -- You are running version $nextflow.version"
}
if (params.dti_shells){
log.info "DTI shells extracted: $params.dti_shells"
}
else{
log.info "Max DTI shell extracted: $params.max_dti_shell_value"
}
if (params.fodf_shells){
log.info "FODF shells extracted: $params.fodf_shells"
}
else{
log.info "Min FODF shell extracted: $params.min_fodf_shell_value"
}
labels_for_reg = Channel.empty()
freesurfer_path = Channel.from("")
bidsignore_path = Channel.from("")
if (params.input && !(params.bids && params.bids_config)){
log.info "Input: $params.input"
root = file(params.input)
Channel
.fromFilePairs("$root/**/*{bval,bvec,dwi.nii.gz,t1.nii.gz}",
size: 4,
maxDepth:1,
flat: true) {it.parent.name}
.into{data; data_for_sid}
data_for_sid.map{[it[0]]}.set{ch_sid_dwi}
labels_for_reg = Channel
.fromFilePairs("$root/**/*{aparc+aseg.nii.gz,wmparc.nii.gz}",
size: 2,
maxDepth:1,
flat: true) {it.parent.name}
data.map{[it[0], "_", it[1..3], it[4], params.readout, params.encoding_direction].flatten()}
.into{in_data; check_subjects_number}
Channel
.fromPath("$root/**/*rev_b0.nii.gz",
maxDepth:1)
.map{[it.parent.name, it]}
.tap{rev_b0_for_topup; check_simple_rev_b0}
.map{ [it[0]] }
.into{sid_rev_b0_included; sid_rev_b0_included_for_eddy_topup; sid_rev_b0_for_prepare_topup_dwi}
Channel.empty().into{sid_rev_dwi_included; sid_rev_dwi_included_for_eddy; sid_rev_dwi_for_prepare_topup_for_dwi; sid_rev_dwi_included_for_topup; sid_rev_dwi_for_topup; check_rev_number}
Channel.empty().into{ch_sid_b0; complex_rev_b0_for_topup; check_complex_rev_b0}
}
else if (params.bids || params.bids_config){
if (!params.bids_config) {
log.info "Input BIDS: $params.bids"
if (params.fs) {
freesurfer_path = file(params.fs)
log.info "Freesurfer path: $params.fs"
}
if (params.bidsignore) {
bidsignore_path = file(params.bidsignore)
log.info "BIDSignore path: $params.bidsignore"
}
log.info "Clean_bids: $params.clean_bids"
log.info ""
bids = file(params.bids)
process Read_BIDS {
publishDir = params.Read_BIDS_Publish_Dir
scratch = false
stageInMode = 'symlink'
tag = {"Read_BIDS"}
errorStrategy = { task.attempt <= 3 ? 'retry' : 'terminate' }
input:
file(bids_folder) from bids
file(fs_folder) from freesurfer_path
file(bidsignore) from bidsignore_path
output:
file "tractoflow_bids_struct.json" into bids_struct
script:
clean_flag = params.clean_bids ? '--clean ' : ''
"""
scil_validate_bids.py $bids_folder tractoflow_bids_struct.json\
--readout $params.readout $clean_flag\
${!fs_folder.empty() ? "--fs $fs_folder" : ""}\
${!bidsignore.empty() ? "--bids_ignore $bidsignore" : ""}\
-v
"""
}
}
else {
log.info "BIDS config: $params.bids_config"
config = file(params.bids_config)
bids_struct = Channel.from(config)
}
ch_in_data = Channel.create()
ch_sid_rev_dwi = Channel.create()
ch_sid_rev_b0 = Channel.create()
ch_sid_dwi = Channel.create()
ch_sid_b0 = Channel.create()
ch_complex_rev_b0 = Channel.create()
ch_simple_rev_b0 = Channel.create()
labels_for_reg = Channel.create()
bids_struct.map{it ->
jsonSlurper = new JsonSlurper()
data = jsonSlurper.parseText(it.getText())
for (item in data){
sid = "sub-" + item.subject
if (item.session){
sid += "_ses-" + item.session
}
if (item.run){
sid += "_run-" + item.run
}
for (key in item.keySet()){
if(item[key] == 'todo'){
error "Error ~ Please look at your tractoflow_bids_struct.json " +
"in Read_BIDS folder.\nPlease fix todo fields and give " +
"this file in input using --bids_config option instead of " +
"using --bids."
}
else if (item[key] == 'error_readout'){
error "Error ~ Please look at your tractoflow_bids_struct.json " +
"in Read_BIDS folder.\nPlease fix error_readout fields. "+
"This error indicate that readout time looks wrong.\n"+
"Please correct the value or remove the subject in the json and " +
"give the updated file in input using --bids_config option instead of " +
"using --bids."
}
}
sub = [sid, "_", file(item.bval), file(item.bvec), file(item.dwi),
file(item.t1), item.TotalReadoutTime, item.DWIPhaseEncodingDir[0]]
ch_in_data.bind(sub)
ch_sid_dwi.bind([sid])
if(item.rev_topup) {
ch_sid_rev_b0.bind([sid])
if(item.topup) {
ch_sid_b0.bind([sid])
sub_complex_rev_b0 = [sid, file(item.rev_topup), file(item.topup)]
ch_complex_rev_b0.bind(sub_complex_rev_b0)
}
else{
sub_simple_rev_b0 = [sid, file(item.rev_topup)]
ch_simple_rev_b0.bind(sub_simple_rev_b0)
}
}
if(item.rev_dwi){
ch_rev_in_data = [sid, "_rev_", file(item.rev_bval), file(item.rev_bvec), file(item.rev_dwi),
file(item.t1), item.TotalReadoutTime, item.DWIPhaseEncodingDir[0]]
ch_sid_rev_dwi.bind([sid])
ch_in_data.bind(ch_rev_in_data)
}
if(item.wmparc) {
sub_labels_for_reg = [sid, file(item.aparc_aseg), file(item.wmparc)]
labels_for_reg.bind(sub_labels_for_reg)
}
}
ch_sid_rev_dwi.close()
ch_sid_rev_b0.close()
ch_sid_dwi.close()
ch_sid_b0.close()
ch_in_data.close()
ch_simple_rev_b0.close()
ch_complex_rev_b0.close()
labels_for_reg.close()
}
Channel.empty().into{sid_rev_dwi_included; sid_rev_b0_for_prepare_topup_dwi; sid_rev_dwi_included_for_topup; check_rev_number}
ch_sid_rev_dwi.into{sid_rev_dwi_included; sid_rev_dwi_included_for_topup; sid_rev_dwi_for_prepare_topup_for_dwi; sid_rev_dwi_included_for_eddy; check_rev_number}
ch_sid_rev_b0.into{sid_rev_b0_included; sid_rev_dwi_for_topup; sid_rev_b0_included_for_eddy_topup; sid_rev_b0_for_prepare_topup_dwi}
ch_in_data.into{in_data; check_subjects_number}
ch_simple_rev_b0.into{rev_b0_for_topup; check_simple_rev_b0}
ch_complex_rev_b0.into{complex_rev_b0_for_topup; check_complex_rev_b0}
}
else {
error "Error ~ Please use --input, --bids or --bids_config for the input data."
}
check_subjects_number.map{[it[0]]}.unique().set{unique_subjects_number}
if (params.sh_fitting && !params.sh_fitting_shells){
error "Error ~ Please set the SH fitting shell to use."
}
if (params.pft_seeding_mask_type != "wm" && params.pft_seeding_mask_type != "interface" && params.pft_seeding_mask_type != "fa"){
error "Error ~ --pft_seeding_mask_type can only take wm, interface or fa. Please select one of these choices"
}
if (params.local_seeding_mask_type != "wm" && params.local_seeding_mask_type != "fa"){
error "Error ~ --local_seeding_mask_type can only take wm or fa. Please select one of these choices"
}
if (params.local_tracking_mask_type != "wm" && params.local_tracking_mask_type != "fa"){
error "Error ~ --local_tracking_mask_type can only take wm or fa. Please select one of these choices"
}
if (params.local_algo != "det" && params.local_algo != "prob"){
error "Error ~ --local_algo can only take det or prob. Please select one of these choices"
}
if (params.pft_algo != "det" && params.pft_algo != "prob"){
error "Error ~ --pft_algo can only take det or prob. Please select one of these choices"
}
if (params.local_seeding != "nt" && params.local_seeding != "npv"){
error "Error ~ --local_seeding can only take nt or npv. Please select one of these choices"
}
if (params.pft_seeding != "nt" && params.pft_seeding != "npv"){
error "Error ~ --pft_seeding can only take nt or npv. Please select one of these choices"
}
if (params.run_pft_tracking && workflow.profile.contains("ABS")){
error "Error ~ PFT tracking cannot be run with Atlas Based Segmentation (ABS) profile"
}
if (params.bids && workflow.profile.contains("ABS") && !params.fs){
error "Error ~ --bids parameter cannot be run with Atlas Based Segmentation (ABS) profile"
}
(dwi, gradients, t1, readout_encoding) = in_data
.map{sid, rev_flag, bvals, bvecs, dwi, t1, readout, encoding -> [tuple(sid, rev_flag, dwi),
tuple(sid, rev_flag, bvals, bvecs),
tuple(sid, t1),
tuple(sid, readout, encoding)]}
.separate(4)
t1.unique()
.into{t1_for_denoise; t1_for_test_denoise}
check_complex_rev_b0.concat(check_simple_rev_b0).count().into{rev_b0_counter; number_rev_b0_for_compare}
unique_subjects_number.count().into{number_subj_for_null_check; number_subj_for_compare}
check_rev_number.count().into{number_rev_dwi; rev_dwi_counter}
if (params.eddy_cmd == "eddy_cpu" && params.processes_eddy == 1 && params.run_eddy == true){
number_rev_dwi
.subscribe{a -> if (a>0)
error "Error ~ You have some subjects with a reverse encoding DWI.\n" +
"Eddy will take forever to run with this configuration. \nPlease add " +
"-profile use_gpu with a GPU environnement (GPU NVIDIA with cuda) OR increase the number " +
"of processes for this task (--processes_eddy) to be able to analyse this data."}
}
if (!params.run_topup || !params.run_eddy){
number_rev_dwi
.subscribe{a -> if (a>0)
error "Error ~ You have some subjects with a reverse encoding DWI. You MUST run topup and eddy with this kind of acquisition."}
}
number_subj_for_null_check
.subscribe{a -> if (a == 0)
error "Error ~ No subjects found. Please check the naming convention, your --input path or your BIDS folder."}
if (params.set_frf && params.mean_frf){
error "Error ~ --set_frf and --mean_frf are activated. Please choose only one of these options. "
}
if (params.run_topup){
number_subj_for_compare
.concat(number_rev_b0_for_compare)
.toList()
.subscribe{a, b -> if (a != b && b > 0)
error "Error ~ Some subjects have a reversed phase encoded b=0 and others don't.\n" +
"Please be sure to have the same acquisitions for all subjects."}
}
dwi.into{dwi_for_prelim_bet; dwi_for_denoise; dwi_for_test_denoise;truc}
if (params.pft_random_seed instanceof String){
pft_random_seed = params.pft_random_seed?.tokenize(',')
}
else{
pft_random_seed = params.pft_random_seed
}
if (params.local_random_seed instanceof String){
local_random_seed = params.local_random_seed?.tokenize(',')
}
else{
local_random_seed = params.local_random_seed
}
gradients
.into{gradients_for_prelim_bet; gradients_for_eddy;
gradients_for_prepare_topup;
gradients_for_prepare_dwi_for_eddy;
gradients_for_eddy_topup; gradients_for_test_eddy_topup}
readout_encoding
.into{readout_encoding_for_topup; readout_encoding_for_eddy;
readout_encoding_for_eddy_topup}
ch_sid_dwi
.into{ch_sid_dwi_for_rev; ch_sid_dwi_for_dwi}
process README {
cpus 1
publishDir = params.Readme_Publish_Dir
tag = "README"
output:
file "readme.txt"
script:
String list_options = new String();
for (String item : params) {
list_options += item + "\n"
}
"""
echo "TractoFlow pipeline\n" >> readme.txt
echo "Start time: $workflow.start\n" >> readme.txt
echo "[Command-line]\n$workflow.commandLine\n" >> readme.txt
echo "[Git Info]\n" >> readme.txt
echo "$workflow.repository - $workflow.revision [$workflow.commitId]\n" >> readme.txt
echo "[Options]\n" >> readme.txt
echo "$list_options" >> readme.txt
"""
}
dwi_for_prelim_bet
.combine(gradients_for_prelim_bet, by: [0,1])
.set{dwi_gradient_for_prelim_bet}
process Bet_Prelim_DWI {
cpus 2
input:
set sid, val(rev), file(dwi), file(bval), file(bvec) from dwi_gradient_for_prelim_bet
val(rev_b0_count) from rev_b0_counter
val(rev_dwi_count) from rev_dwi_counter
output:
set sid, "${sid}__b0_bet_mask_dilated.nii.gz" into\
b0_mask_for_eddy
file "${sid}__b0_bet.nii.gz"
file "${sid}__b0_bet_mask.nii.gz"
when:
(rev_b0_count == 0 && rev_dwi_count == 0 && params.run_eddy) || (!params.run_topup && params.run_eddy)
script:
"""
export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=1
export OMP_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
scil_extract_b0.py $dwi $bval $bvec ${sid}__b0.nii.gz --mean\
--b0_thr $params.b0_thr_extract_b0 --force_b0_threshold
bet ${sid}__b0.nii.gz ${sid}__b0_bet.nii.gz -m -R -f $params.bet_prelim_f
scil_image_math.py convert ${sid}__b0_bet_mask.nii.gz ${sid}__b0_bet_mask.nii.gz --data_type uint8 -f
maskfilter ${sid}__b0_bet_mask.nii.gz dilate ${sid}__b0_bet_mask_dilated.nii.gz\
--npass $params.dilate_b0_mask_prelim_brain_extraction -nthreads 1
mrcalc ${sid}__b0.nii.gz ${sid}__b0_bet_mask_dilated.nii.gz\
-mult ${sid}__b0_bet.nii.gz -quiet -force -nthreads 1
"""
}
process Denoise_DWI {
cpus params.processes_denoise_dwi
label 'big_mem'
input:
set sid, val(rev), file(dwi) from dwi_for_denoise
output:
set sid, val(rev), "${sid}_${rev}dwi_denoised.nii.gz" into\
dwi_denoised_for_mix
when:
params.run_dwi_denoising
script:
// The denoised DWI is clipped to 0 since negative values
// could have been introduced.
"""
export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=1
export OMP_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
dwidenoise $dwi dwi_denoised.nii.gz -extent $params.extent -nthreads $task.cpus
fslmaths dwi_denoised.nii.gz -thr 0 ${sid}_${rev}dwi_denoised.nii.gz
"""
}
dwi_for_test_denoise
.map{it -> if(!params.run_dwi_denoising){it}}
.mix(dwi_denoised_for_mix)
.into{dwi_for_gibbs; dwi_for_test_gibbs}
process Gibbs_correction {
cpus params.processes_denoise_dwi
input:
set sid, val(rev), file(dwi) from dwi_for_gibbs
output:
set sid, val(rev), "${sid}_${rev}dwi_gibbs_corrected.nii.gz" into\
dwi_gibbs_for_mix
when:
params.run_gibbs_correction
script:
"""
export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=1
export OMP_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
mrdegibbs $dwi ${sid}_${rev}dwi_gibbs_corrected.nii.gz -nthreads $task.cpus
"""
}
dwi_for_test_gibbs
.map{it -> if(!params.run_gibbs_correction){it}}
.mix(dwi_gibbs_for_mix)
.into{dwi_for_eddy; dwi_for_topup; dwi_for_eddy_topup; dwi_for_test_eddy_topup}
ch_sid_b0
.mix(ch_sid_dwi_for_dwi)
.collect()
.map { it
// Group by sid
.groupBy { it }
// Check size
.collect { key, values -> [key, values.size()] }
// Take only the sid appearing one time (they don't have a b0)
.findAll { it[1] == 1}
}
.flatMap()
.map {[it[0]]}
.join(sid_rev_b0_for_prepare_topup_dwi.concat(sid_rev_dwi_for_prepare_topup_for_dwi))
.map {[it, "_"]}
.set{sid_dwi_for_prepare_topup}
sid_rev_b0_included
.mix(sid_rev_dwi_included)
.collect()
.map { it
// Group by sid
.groupBy { it }
// Check size
.collect { key, values -> [key, values.size()] }
// Take only the sid appearing one time (they don't have a b0)
.findAll { it[1] == 1 }
}
.flatMap()
.map {[it[0]]}
.join(sid_rev_dwi_included_for_topup)
.map{[it, "_rev_"]}
.set{sid_rev_dwi_for_prepare_topup}
dwi_for_topup
.combine(sid_dwi_for_prepare_topup.concat(sid_rev_dwi_for_prepare_topup), by: [0,1])
.join(gradients_for_prepare_topup)
.map{ [it[0], it[1], it[2], it[4], it[5]] }
.set{dwi_gradients_rev_b0_for_prepare_topup}
process Prepare_for_Topup {
cpus 2
input:
set sid, val(rev), file(dwi), file(bval), file(bvec)\
from dwi_gradients_rev_b0_for_prepare_topup
output:
set sid, "${sid}_${rev}b0_mean.nii.gz", val(rev) into simple_b0_for_topup
when:
params.run_topup && params.run_eddy
script:
"""
scil_extract_b0.py $dwi $bval $bvec ${sid}_${rev}b0_mean.nii.gz --mean\
--b0_thr $params.b0_thr_extract_b0 --force_b0_threshold
"""
}
simple_b0_for_topup
.branch{
forward_b0: it[2] == "_"
return it[0..1]
reverse_b0: it[2] == "_rev_"
return it[0..1]
}
.set{branch_b0_for_topup}
branch_b0_for_topup.reverse_b0
.mix(rev_b0_for_topup)
.join(branch_b0_for_topup.forward_b0)
.mix(complex_rev_b0_for_topup)
.join(readout_encoding_for_topup)
.set{rev_b0_with_readout_encoding_for_topup}
process Topup {
cpus 4
input:
set sid, file(rev_b0), file(b0), readout, encoding\
from rev_b0_with_readout_encoding_for_topup
output:
set sid, "${sid}__corrected_b0s.nii.gz", "${params.prefix_topup}_fieldcoef.nii.gz",
"${params.prefix_topup}_movpar.txt" into topup_files_for_eddy_topup
file "${sid}__rev_b0_warped.nii.gz"
file "${sid}__rev_b0_mean.nii.gz"
when:
params.run_topup && params.run_eddy
script:
"""
export OMP_NUM_THREADS=$task.cpus
export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=1
export OPENBLAS_NUM_THREADS=1
export ANTS_RANDOM_SEED=1234
scil_image_math.py concatenate $rev_b0 $rev_b0 ${sid}__concatenated_rev_b0.nii.gz
scil_image_math.py mean ${sid}__concatenated_rev_b0.nii.gz ${sid}__rev_b0_mean.nii.gz
antsRegistrationSyNQuick.sh -d 3 -f $b0 -m ${sid}__rev_b0_mean.nii.gz -o output -t r -e 1
mv outputWarped.nii.gz ${sid}__rev_b0_warped.nii.gz
scil_prepare_topup_command.py $b0 ${sid}__rev_b0_warped.nii.gz\
--config $params.config_topup\
--encoding_direction $encoding\
--readout $readout --out_prefix $params.prefix_topup\
--out_script
sh topup.sh
cp corrected_b0s.nii.gz ${sid}__corrected_b0s.nii.gz
"""
}
dwi_for_eddy_topup.into{complex_dwi_for_eddy_topup; simple_dwi_for_eddy_topup}
// Extract subjects with reverse DWI for Prepare_dwi_for_eddy
complex_dwi_for_eddy_topup
.join(gradients_for_prepare_dwi_for_eddy)
.map{[it[0], it[1], it[2], it[4], it[5]]}
.set{dwi_gradient_for_prepare_dwi_for_eddy}
sid_rev_dwi_included_for_eddy
.combine(dwi_gradient_for_prepare_dwi_for_eddy, by: 0)
.branch{
forward_dwi: it[1] == "_"
return [it[0]] + it[2..-1]
reverse_dwi: it[1] == "_rev_"
return [it[0]] + it[2..-1]
}
.set{branch_dwi_gradient_for_prepare_dwi_for_eddy}
branch_dwi_gradient_for_prepare_dwi_for_eddy.forward_dwi
.join(branch_dwi_gradient_for_prepare_dwi_for_eddy.reverse_dwi)
.set{dwi_rev_gradient_for_prepare_dwi_for_eddy}
process Prepare_dwi_for_eddy {
cpus 2
input:
set sid, file(dwi), file(bval), file(bvec), file(rev_dwi), file(rev_bval), \
file(rev_bvec) from dwi_rev_gradient_for_prepare_dwi_for_eddy
output:
set sid, "${sid}__concatenated_dwi.nii.gz", "${sid}__concatenated_dwi.bval", "${sid}__concatenated_dwi.bvec", env(rev_number_dir) into concatenated_dwi_for_eddy
when:
params.run_topup && params.run_eddy
script:
"""
scil_concatenate_dwi.py ${sid}__concatenated_dwi.nii.gz ${sid}__concatenated_dwi.bval ${sid}__concatenated_dwi.bvec -f\
--in_dwis ${dwi} ${rev_dwi} --in_bvals ${bval} ${rev_bval}\
--in_bvecs ${bvec} ${rev_bvec}
rev_number_dir=\$(scil_print_header.py ${rev_dwi} --key dim | sed "s/ / /g" | sed "s/ / /g" | rev | cut -d' ' -f4-4 | rev)
"""
}
// Extract subjects with reverse b0 images for Eddy
expl1 = Channel.value(0)
gradients_for_eddy_topup
.filter{ it[1] == "_" }
.set{simple_gradients_for_eddy_topup}
sid_rev_b0_included_for_eddy_topup
.combine(simple_dwi_for_eddy_topup, by: 0)
.filter{ it[1] == "_" }
.join(simple_gradients_for_eddy_topup)
.merge(expl1)
.map{[it[0], it[2], it[4], it[5], it[6]]}
.set{simple_dwi_gradients_for_eddy_topup}
concatenated_dwi_for_eddy
.mix(simple_dwi_gradients_for_eddy_topup)
.map{ [it[0], it[1], it[2], it[3], it[4]] }
.join(topup_files_for_eddy_topup)
.join(readout_encoding_for_eddy_topup)
.set{dwi_gradients_mask_topup_files_for_eddy_topup}
process Eddy_Topup {
cpus { params.processes_eddy * task.attempt }
memory { 5.GB * task.attempt }
input:
set sid, file(dwi), file(bval), file(bvec), val(number_rev_dwi), file(b0s_corrected),
file(field), file(movpar), readout, encoding\
from dwi_gradients_mask_topup_files_for_eddy_topup
val(rev_b0_count) from rev_b0_counter
val(rev_dwi_count) from rev_dwi_counter
output:
set sid, "${sid}__dwi_corrected.nii.gz" into\
dwi_from_eddy_topup
set sid, "${sid}__bval_eddy", "${sid}__dwi_eddy_corrected.bvec" into\
gradients_from_eddy_topup
file "${sid}__b0_bet_mask.nii.gz"
when:
(rev_b0_count > 0 || rev_dwi_count > 0) && params.run_topup && params.run_eddy
// Corrected DWI is clipped to ensure there are no negative values
// introduced by Eddy.
script:
slice_drop_flag=""
if (params.use_slice_drop_correction)
slice_drop_flag="--slice_drop_correction"
"""
export OMP_NUM_THREADS=$task.cpus
export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=$task.cpus
export OPENBLAS_NUM_THREADS=1
mrconvert $b0s_corrected b0_corrected.nii.gz -coord 3 0 -axes 0,1,2 -nthreads 1
bet b0_corrected.nii.gz ${sid}__b0_bet.nii.gz -m -R\
-f $params.bet_topup_before_eddy_f
scil_prepare_eddy_command.py $dwi $bval $bvec ${sid}__b0_bet_mask.nii.gz\
--topup $params.prefix_topup --eddy_cmd $params.eddy_cmd\
--b0_thr $params.b0_thr_extract_b0\
--encoding_direction $encoding\
--readout $readout --out_script --fix_seed\
--n_reverse ${number_rev_dwi}\
--lsr_resampling\
$slice_drop_flag
echo "--very_verbose" >> eddy.sh
sh eddy.sh
fslmaths dwi_eddy_corrected.nii.gz -thr 0 ${sid}__dwi_corrected.nii.gz
if [[ $number_rev_dwi -eq 0 ]]
then
mv dwi_eddy_corrected.eddy_rotated_bvecs ${sid}__dwi_eddy_corrected.bvec
mv $bval ${sid}__bval_eddy
else
scil_validate_and_correct_eddy_gradients.py dwi_eddy_corrected.eddy_rotated_bvecs $bval ${number_rev_dwi} ${sid}__dwi_eddy_corrected.bvec ${sid}__bval_eddy
fi
"""
}
dwi_for_eddy
.combine(gradients_for_eddy, by: [0,1])
.filter{ it[1] == "_" }
.map{ [it[0], it[2], it[3], it[4]] }
.join(b0_mask_for_eddy)
.join(readout_encoding_for_eddy)
.set{dwi_gradients_mask_topup_files_for_eddy}
process Eddy {
cpus { params.processes_eddy * task.attempt }
memory { 5.GB * task.attempt }
input:
set sid, file(dwi), file(bval), file(bvec), file(mask), readout, encoding\
from dwi_gradients_mask_topup_files_for_eddy
val(rev_b0_count) from rev_b0_counter
val(rev_dwi_count) from rev_dwi_counter
output:
set sid, "${sid}__dwi_corrected.nii.gz" into\
dwi_from_eddy
set sid, "${sid}__bval_eddy", "${sid}__dwi_eddy_corrected.bvec" into\
gradients_from_eddy
when:
(rev_b0_count == 0 && rev_dwi_count == 0 && params.run_eddy) || (!params.run_topup && params.run_eddy)
// Corrected DWI is clipped to 0 since Eddy can introduce negative values.
script:
slice_drop_flag=""
if (params.use_slice_drop_correction) {
slice_drop_flag="--slice_drop_correction"
}
"""
export OMP_NUM_THREADS=$task.cpus
export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=$task.cpus
export OPENBLAS_NUM_THREADS=1
scil_prepare_eddy_command.py $dwi $bval $bvec $mask\
--eddy_cmd $params.eddy_cmd --b0_thr $params.b0_thr_extract_b0\
--encoding_direction $encoding\
--readout $readout --out_script --fix_seed\
$slice_drop_flag
sh eddy.sh
fslmaths dwi_eddy_corrected.nii.gz -thr 0 ${sid}__dwi_corrected.nii.gz
mv dwi_eddy_corrected.eddy_rotated_bvecs ${sid}__dwi_eddy_corrected.bvec
mv $bval ${sid}__bval_eddy
"""
}
dwi_for_test_eddy_topup
.map{it -> if(!params.run_eddy){it}}
.filter{ it[1] == "_" }
.map{ [it[0], it[2]] }
.set{dwi_for_skip_eddy_topup}
gradients_for_test_eddy_topup
.map{it -> if(!params.run_eddy){it}}
.filter{ it[1] == "_" }
.map{ [it[0], it[2], it[3]] }
.set{gradients_for_skip_eddy_topup}
dwi_from_eddy
.mix(dwi_from_eddy_topup)
.mix(dwi_for_skip_eddy_topup)
.set{dwi_for_bet}
gradients_from_eddy
.mix(gradients_from_eddy_topup)
.mix(gradients_for_skip_eddy_topup)
.into{gradients_for_extract_b0;
gradients_for_dti_shell;
gradients_for_fodf_shell;
gradients_for_normalize;
gradients_for_bet;
gradients_for_sh_fitting_shell}
dwi_for_bet
.join(gradients_for_bet)
.set{dwi_gradients_for_bet}
process Bet_DWI {
cpus 2
label 'big_mem'
input:
set sid, file(dwi), file(bval), file(bvec) from dwi_gradients_for_bet
output:
set sid, "${sid}__b0_bet.nii.gz", "${sid}__b0_bet_mask.nii.gz" into\
b0_and_mask_for_crop
set sid, "${sid}__dwi_bet.nii.gz", "${sid}__b0_bet.nii.gz",
"${sid}__b0_bet_mask.nii.gz" into dwi_b0_b0_mask_for_n4
file "${sid}__b0_no_bet.nii.gz"
script:
"""
export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=1
export OMP_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
scil_extract_b0.py $dwi $bval $bvec ${sid}__b0_no_bet.nii.gz --mean\
--b0_thr $params.b0_thr_extract_b0 --force_b0_threshold
bet ${sid}__b0_no_bet.nii.gz ${sid}__b0_bet.nii.gz -m -R -f $params.bet_dwi_final_f
scil_image_math.py convert ${sid}__b0_bet_mask.nii.gz ${sid}__b0_bet_mask.nii.gz --data_type uint8 -f
mrcalc $dwi ${sid}__b0_bet_mask.nii.gz -mult ${sid}__dwi_bet.nii.gz -quiet -nthreads 1
"""
}
process N4_DWI {
cpus 1
label 'big_mem'
input:
set sid, file(dwi), file(b0), file(b0_mask)\
from dwi_b0_b0_mask_for_n4
output:
set sid, "${sid}__dwi_n4.nii.gz" into dwi_for_crop
script:
"""
export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=$task.cpus
export OMP_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
N4BiasFieldCorrection -i $b0\
-o [${sid}__b0_n4.nii.gz, bias_field_b0.nii.gz]\
-c [300x150x75x50, 1e-6] -v 1
scil_apply_bias_field_on_dwi.py $dwi bias_field_b0.nii.gz\
${sid}__dwi_n4.nii.gz --mask $b0_mask -f
"""
}
dwi_for_crop
.join(b0_and_mask_for_crop)
.set{dwi_and_b0_mask_b0_for_crop}
process Crop_DWI {
cpus 1
label 'big_mem'
input:
set sid, file(dwi), file(b0), file(b0_mask) from dwi_and_b0_mask_b0_for_crop
output:
set sid, "${sid}__dwi_cropped.nii.gz",
"${sid}__b0_mask_cropped.nii.gz" into dwi_mask_for_normalize
set sid, "${sid}__b0_mask_cropped.nii.gz" into mask_for_resample
file "${sid}__b0_cropped.nii.gz"
script:
"""
export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=1
export OMP_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
scil_crop_volume.py $dwi ${sid}__dwi_cropped.nii.gz -f\
--output_bbox dwi_boundingBox.pkl -f
scil_crop_volume.py $b0 ${sid}__b0_cropped.nii.gz\
--input_bbox dwi_boundingBox.pkl -f
scil_crop_volume.py $b0_mask ${sid}__b0_mask_cropped.nii.gz\
--input_bbox dwi_boundingBox.pkl -f
scil_image_math.py convert ${sid}__b0_mask_cropped.nii.gz ${sid}__b0_mask_cropped.nii.gz --data_type uint8 -f
"""
}
process Denoise_T1 {
cpus params.processes_denoise_t1
input:
set sid, file(t1) from t1_for_denoise
output:
set sid, "${sid}__t1_denoised.nii.gz" into t1_for_mix_n4
when:
params.run_t1_denoising
script:
"""
export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=1
export OMP_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
scil_run_nlmeans.py $t1 ${sid}__t1_denoised.nii.gz 1 \
--processes $task.cpus -f
"""
}
t1_for_test_denoise
.map{it -> if(!params.run_t1_denoising){it}}
.mix(t1_for_mix_n4)
.set{t1_for_n4}
process N4_T1 {
cpus 1
input:
set sid, file(t1) from t1_for_n4
output:
set sid, "${sid}__t1_n4.nii.gz" into t1_for_resample, t1_for_test_resample
script:
"""
export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=$task.cpus
export OMP_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
N4BiasFieldCorrection -i $t1\
-o [${sid}__t1_n4.nii.gz, bias_field_t1.nii.gz]\
-c [300x150x75x50, 1e-6] -v 1