tracula.py

import os
from glob import glob

from nipype import config
config.enable_provenance()

from nipype import Node, Function, Workflow, IdentityInterface
from nipype.interfaces.io import SelectFiles, DataSink

import os
from glob import glob

data_dir = '/om/user/satra/projects/TEXASTBI/data/'
tracula_dir = '/om/user/satra/projects/TEXASTBI/tracula'
sids = [val.split('/')[-3] for val in glob(data_dir + 'S*/dmri/*AP*.nii.gz')]

if not os.path.exists(tracula_dir):
    os.mkdir(tracula_dir)

def run_prep(sid, template, data_dir):
    from glob import glob
    import os
    nifti = os.path.abspath(glob(os.path.join(data_dir, '%s/dmri/*AP*.nii.gz' % sid))[0])
    bvec = os.path.abspath(glob(os.path.join(data_dir, '%s/dmri/*.bvecs' % sid))[0])
    bval = os.path.abspath(glob(os.path.join(data_dir, '%s/dmri/*.bvals' % sid))[0])
    from string import Template
    with open(template, 'rt') as fp:
        tpl = Template(fp.read())
    out = tpl.substitute(subjects=sid, bvec=bvec, bval=bval, niftis=nifti)
    config_file = os.path.join(os.getcwd(), 'config_%s' % sid)
    with open(config_file, 'wt') as fp:
        fp.write(out)
    from nipype import config
    config.enable_provenance()
    from nipype.interfaces.base import CommandLine
    from nipype.pipeline.engine import Node
    node = Node(CommandLine('trac-all -prep -c %s' % config_file, terminal_output='allatonce'),
                name='trac-prep-%s' % sid)
    node.base_dir = os.getcwd()
    node.run()
    return sid, config_file

def run_bedpost(sid, tracula_dir):
    import os
    from nipype import config
    config.enable_provenance()
    from nipype.interfaces.base import CommandLine
    pwd = os.getcwd()
    os.chdir(os.path.join(tracula_dir, sid))
    bedpost = CommandLine('bedpostx_gpu dmri', terminal_output='allatonce')
    bedpost.run()
    os.chdir(pwd)
    return sid

def run_path(sid, config_file):
    import os
    from nipype import config
    config.enable_provenance()
    from nipype.interfaces.base import CommandLine
    from nipype.pipeline.engine import Node
    node = Node(CommandLine('trac-all -path -c %s' % config_file, terminal_output='allatonce'),
                name='trac-path-%s' % sid)
    node.base_dir = os.getcwd()
    node.run()
    return sid

def dmri_recon(sid, tracula_dir, recon='csd', num_threads=1):
    import os
    oldval = None
    if 'MKL_NUM_THREADS' in os.environ:
        oldval = os.environ['MKL_NUM_THREADS']
    os.environ['MKL_NUM_THREADS'] = '%d' % num_threads
    ompoldval = None
    if 'OMP_NUM_THREADS' in os.environ:
        ompoldval = os.environ['OMP_NUM_THREADS']
    os.environ['OMP_NUM_THREADS'] = '%d' % num_threads
    import nibabel as nib
    import numpy as np
    from glob import glob


    fimg = os.path.abspath(glob(os.path.join(tracula_dir, '%s/dmri/dwi.nii.gz' % sid))[0])
    fbvec = os.path.abspath(glob(os.path.join(tracula_dir, '%s/dmri/bvecs' % sid))[0])
    fbval = os.path.abspath(glob(os.path.join(tracula_dir, '%s/dmri/bvals' % sid))[0])
    img = nib.load(fimg)
    data = img.get_data()
    
    prefix = sid

    from dipy.io import read_bvals_bvecs
    from dipy.core.gradients import vector_norm
    bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
    b0idx = []
    for idx, val in enumerate(bvals):
        if val < 1:
            pass
            #bvecs[idx] = [1, 0, 0]
        else:
            b0idx.append(idx)
    bvecs[b0idx, :] = bvecs[b0idx, :]/vector_norm(bvecs[b0idx])[:, None]
    
    from dipy.core.gradients import gradient_table
    gtab = gradient_table(bvals, bvecs)
    
    from dipy.reconst.csdeconv import auto_response
    response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
    
    #from dipy.segment.mask import median_otsu
    #b0_mask, mask = median_otsu(data[:, :, :, b0idx].mean(axis=3).squeeze(), 4, 4)
    
    fmask1 = os.path.abspath(glob(os.path.join(tracula_dir, 
                                              '%s/dlabel/diff/aparc+aseg_mask.bbr.nii.gz' % sid))[0])
    fmask2 = os.path.abspath(glob(os.path.join(tracula_dir, 
                                              '%s/dlabel/diff/notventricles.bbr.nii.gz' % sid))[0])
    mask = (nib.load(fmask1).get_data() > 0.5) * nib.load(fmask2).get_data()

    useFA = True
    if recon == 'csd':
        from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
        model = ConstrainedSphericalDeconvModel(gtab, response)
        useFA = True
    elif recon == 'csa':
        from dipy.reconst.shm import CsaOdfModel, normalize_data
        model = CsaOdfModel(gtab, 4)
        useFA = False
    else:
        raise ValueError('only csd, csa supported currently')
        from dipy.reconst.dsi import (DiffusionSpectrumDeconvModel,
                                      DiffusionSpectrumModel)
        model = DiffusionSpectrumDeconvModel(gtab)
    #fit = model.fit(data)
    
    from dipy.data import get_sphere
    sphere = get_sphere('symmetric724')
    #odfs = fit.odf(sphere)
    
    from dipy.reconst.peaks import peaks_from_model
    peaks = peaks_from_model(model=model,
                             data=data,
                             sphere=sphere,
                             mask=mask,
                             return_sh=True,
                             return_odf=False,
                             normalize_peaks=True,
                             npeaks=5,
                             relative_peak_threshold=.5,
                             min_separation_angle=25,
                             parallel=num_threads > 1,
                             nbr_processes=num_threads)
                                      
    from dipy.reconst.dti import TensorModel
    tenmodel = TensorModel(gtab)
    tenfit = tenmodel.fit(data, mask)
    
    from dipy.reconst.dti import fractional_anisotropy
    FA = fractional_anisotropy(tenfit.evals)
    FA[np.isnan(FA)] = 0
    fa_img = nib.Nifti1Image(FA, img.get_affine())
    tensor_fa_file = os.path.abspath('%s_tensor_fa.nii.gz' % prefix)
    nib.save(fa_img, tensor_fa_file)
    
    evecs = tenfit.evecs
    evec_img = nib.Nifti1Image(evecs, img.get_affine())
    tensor_evec_file = os.path.abspath('%s_tensor_evec.nii.gz' % prefix)
    nib.save(evec_img, tensor_evec_file)

    #from dipy.reconst.dti import quantize_evecs    
    #peak_indices = quantize_evecs(tenfit.evecs, sphere.vertices)
    #eu = EuDX(FA, peak_indices, odf_vertices = sphere.vertices, a_low=0.2, seeds=10**6, ang_thr=35)

    fa_img = nib.Nifti1Image(peaks.gfa, img.get_affine())
    model_gfa_file = os.path.abspath('%s_%s_gfa.nii.gz' % (prefix, recon))
    nib.save(fa_img, model_gfa_file)
    
    from dipy.tracking.eudx import EuDX
    if useFA:
        eu = EuDX(FA, peaks.peak_indices[..., 0], odf_vertices = sphere.vertices, 
                  a_low=0.1, seeds=10**6, ang_thr=35)
    else:
        eu = EuDX(peaks.gfa, peaks.peak_indices[..., 0], odf_vertices = sphere.vertices, 
                  a_low=0.1, seeds=10**6, ang_thr=35)
    
    #import dipy.tracking.metrics as dmetrics
    streamlines = ((sl, None, None) for sl in eu) # if dmetrics.length(sl) > 15)
    
    hdr = nib.trackvis.empty_header()
    hdr['voxel_size'] = fa_img.get_header().get_zooms()[:3]
    hdr['voxel_order'] = 'LAS'
    hdr['dim'] = FA.shape[:3]
    
    sl_fname = os.path.abspath('%s_%s_streamline.trk' % (prefix, recon))
    
    nib.trackvis.write(sl_fname, streamlines, hdr, points_space='voxel')
    if oldval:
        os.environ['MKL_NUM_THREADS'] = oldval
    else:
        del os.environ['MKL_NUM_THREADS']
    if ompoldval:
        os.environ['OMP_NUM_THREADS'] = ompoldval
    else:
        del os.environ['OMP_NUM_THREADS']
    return tensor_fa_file, tensor_evec_file, model_gfa_file, sl_fname

infosource = Node(IdentityInterface(fields=['subject_id']), name='infosource')
infosource.iterables = ('subject_id', sids)

node1 = Node(Function(input_names=['sid', 'template', 'data_dir'], 
                     output_names=['sid', 'config_file'], function=run_prep), 
            name='trac-prep')
node1.inputs.template = os.path.abspath('tracula_config')
node1.inputs.data_dir = data_dir

node2 = Node(Function(input_names=['sid', 'tracula_dir'], 
                     output_names=['sid'], function=run_bedpost), 
            name='trac-bedp')
node2.inputs.tracula_dir = tracula_dir
node2.plugin_args = {'sbatch_args': '--gres=gpu:1'}

node3 = Node(Function(input_names=['sid', 'config_file'], 
                     output_names=['sid'], function=run_path), 
            name='trac-path')

tracker = Node(Function(input_names=['sid', 'tracula_dir', 'recon', 'num_threads'],
                        output_names=['tensor_fa_file', 'tensor_evec_file', 'model_gfa_file', 
                                      'model_track_file'],
                        function=dmri_recon), name='tracker')
tracker.inputs.recon = 'csd'
tracker.inputs.tracula_dir = tracula_dir
num_threads = 20
tracker.inputs.num_threads = num_threads
tracker.plugin_args = {'sbatch_args': '-p om_interactive  -t 01:00:00 --mem=%dG -N 1 -c %d' % (3 * num_threads,
                                                                                               num_threads),
                       'overwrite': True}

ds = Node(DataSink(parameterization=False), name='sinker')
ds.inputs.base_directory = tracula_dir
ds.plugin_args = {'sbatch_args': '-p om_interactive  -t 00:10:00 -N 1 -c 1',
                  'overwrite': True}

wf = Workflow(name='texas-tracula')

wf.connect(infosource, 'subject_id', node1, 'sid')
wf.connect(node1, 'sid', node2, 'sid')
wf.connect(node1, 'sid', tracker, 'sid')
wf.connect(node2, 'sid', node3, 'sid')
wf.connect(node1, 'config_file', node3, 'config_file')
wf.connect(node1, 'sid', ds, 'container')
wf.connect(tracker, 'tensor_fa_file', ds, 'recon.@fa')
wf.connect(tracker, 'tensor_evec_file', ds, 'recon.@evec')
wf.connect(tracker, 'model_gfa_file', ds, 'recon.@gfa')
wf.connect(tracker, 'model_track_file', ds, 'recon.@track')

wf.base_dir = '/om/scratch/Fri/satra/'

wf.run(plugin='SLURM', plugin_args={'sbatch_args': '-p om_all_nodes  -t 2:00:00 --mem=16384 -N1'})

tracula_config

#
# dmrirc.example
#
# This file contains commands that will be run by trac-all before an analysis.
# It is used to set all parameters needed for the analysis.
#
# Remove a parameter from your dmrirc file if you want use the default value.
# Parameters that don't have default values must be specified.
#
# Any other commands that you might want to run before an analysis can be added
# to this file.
#
# Terms and conditions for use, reproduction, distribution and contribution
# are found in the 'FreeSurfer Software License Agreement' contained
# in the file 'LICENSE' found in the FreeSurfer distribution, and here:
#
# https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSoftwareLicense
#
# Reporting: freesurfer@nmr.mgh.harvard.edu
#
#

# FreeSurfer SUBJECTS_DIR
# T1 images and FreeSurfer segmentations are expected to be found here
# 
setenv SUBJECTS_DIR /om/user/satra/projects/TEXASTBI/fsdata

# Output directory where trac-all results will be saved
# Default: Same as SUBJECTS_DIR
#
set dtroot = /om/user/satra/projects/TEXASTBI/tracula

# Subject IDs
#
set subjlist = ($subjects)

# In case you want to analyze only Huey and Louie
# Default: Run analysis on all subjects
#
# set runlist = (1 3)

# Input diffusion DICOMs (file names relative to dcmroot)
# If original DICOMs don't exist, these can be in other image format
# but then the gradient table and b-value table must be specified (see below)
#
#set dcmroot = /path/to/dicoms/of/ducks
set dcmlist = ($niftis)

# Diffusion gradient tables (if there is a different one for each scan)
# Must be specified if inputs are not MGH DICOMs
# The tables must have either three columns, where each row is a gradient vector
# or three rows, where each column is a gradient vector
# There must be as many gradient vectors as volumes in the diffusion data set
# Default: Read from DICOM header
#
#set bveclist = (...)

# Diffusion gradient table (if using the same one for all scans)
# Must be specified if inputs are not MGH DICOMs
# The table must have either three columns, where each row is a gradient vector
# or three rows, where each column is a gradient vector
# There must be as many gradient vectors as volumes in the diffusion data set
# Default: Read from DICOM header
#
set bvecfile = $bvec

# Diffusion b-value table
# Must be specified if inputs are not MGH DICOMs
# There must be as many b-values as volumes in the diffusion data set
# Default: Read from DICOM header
#
set bvalfile = $bval

# Perform registration-based B0-inhomogeneity compensation?
# Default: 0 (no)
#
set dob0 = 0

# Input B0 field map magnitude DICOMs (file names relative to dcmroot)
# Only used if dob0 = 1
# Default: None
#
# set b0mlist = (huey/fmag/XXX-1.dcm dewey/fmag/XXX-1.dcm louie/fmag/XXX-1.dcm)

# Input B0 field map phase DICOMs (file names relative to dcmroot)
# Only used if dob0 = 1
# Default: None
#
# set b0plist = (huey/fphas/XXX-1.dcm dewey/fphas/XXX-1.dcm louie/fphas/XXX-1.dcm)

# Echo spacing for field mapping sequence (from sequence printout)
# Only used if dob0 = 1
# Default: None
#
# set echospacing = 0.7

# Perform registration-based eddy-current compensation?
# Default: 1 (yes)
#
set doeddy = 1

# Rotate diffusion gradient vectors to match eddy-current compensation?
# Only used if doeddy = 1
# Default: 1 (yes)
#
set dorotbvecs = 1

# Fractional intensity threshold for BET mask extraction from low-b images
# This mask is used only if usemaskanat = 0
# Default: 0.3
#
set thrbet = 0.5

# Perform diffusion-to-T1 registration by flirt?
# Default: 0 (no)
#
set doregflt = 0

# Perform diffusion-to-T1 registration by bbregister?
# Default: 1 (yes)
#
set doregbbr = 1

# Perform registration of T1 to MNI template?
# Default: 1 (yes)
#
set doregmni = 1

# MNI template
# Only used if doregmni = 1
# Default: $$FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz
#
# set mnitemp = /path/to/mni_template.nii.gz

# Perform registration of T1 to CVS template?
# Default: 0 (no)
#
set doregcvs = 0

# CVS template subject ID
# Only used if doregcvs = 1
# Default: cvs_avg35
#
set cvstemp = donald

# Parent directory of the CVS template subject
# Only used if doregcvs = 1
# Default: $$FREESURFER_HOME/subjects
#
set cvstempdir = /path/to/cvs/atlases/of/ducks

# Use brain mask extracted from T1 image instead of low-b diffusion image?
# Has no effect if there is no T1 data
# Default: 1 (yes)
#
set usemaskanat = 1

# Paths to reconstruct
# Default: All paths in the atlas
#
set pathlist = ( lh.cst_AS rh.cst_AS \
                 lh.unc_AS rh.unc_AS \
                 lh.ilf_AS rh.ilf_AS \
                 fmajor_PP fminor_PP \
                 lh.atr_PP rh.atr_PP \
                 lh.ccg_PP rh.ccg_PP \
                 lh.cab_PP rh.cab_PP \
                 lh.slfp_PP rh.slfp_PP \
                 lh.slft_PP rh.slft_PP )

# Number of path control points
# It can be a single number for all paths or a different number for each of the
# paths specified in pathlist
# Default: 7 for the forceps major, 6 for the corticospinal tract,
#          4 for the angular bundle, and 5 for all other paths
#
set ncpts = (6 6 5 5 5 5 7 5 5 5 5 5 4 4 5 5 5 5)

# List of training subjects
# This text file lists the locations of training subject directories
# Default: $$FREESURFER_HOME/trctrain/trainlist.txt
#
set trainfile = $$FREESURFER_HOME/trctrain/trainlist.txt

# Number of "sticks" (anisotropic diffusion compartments) in the bedpostx
# ball-and-stick model
# Default: 2
#
set nstick = 2

# Number of MCMC burn-in iterations
# (Path samples drawn initially by MCMC algorithm and discarded)
# Default: 200
#
set nburnin = 200

# Number of MCMC iterations
# (Path samples drawn by MCMC algorithm and used to estimate path distribution)
# Default: 7500
#
set nsample = 7500

# Frequency with which MCMC path samples are retained for path distribution
# Default: 5 (keep every 5th sample)
#
set nkeep = 5

# Reinitialize path reconstruction?
# This is an option of last resort, to be used only if one of the reconstructed
# pathway distributions looks like a single curve. This is a sign that the
# initial guess for the pathway was problematic, perhaps due to poor alignment
# between the individual and the atlas. Setting the reinit parameter to 1 and
# rerunning "trac-all -prior" and "trac-all -path", only for the specific
# subjects and pathways that had this problem, will attempt to reconstruct them
# with a different initial guess.
# Default: 0 (do not reinitialize)
#
set reinit = 0