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/* Copyright (c) 2008-2025 the MRtrix3 contributors.
*
* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, You can obtain one at http://mozilla.org/MPL/2.0/.
*
* Covered Software is provided under this License on an "as is"
* basis, without warranty of any kind, either expressed, implied, or
* statutory, including, without limitation, warranties that the
* Covered Software is free of defects, merchantable, fit for a
* particular purpose or non-infringing.
* See the Mozilla Public License v. 2.0 for more details.
*
* For more details, see http://www.mrtrix.org/.
*/
#include "command.h"
#include "image.h"
#include "types.h"
#include "math/SH.h"
#include "dwi/directions/set.h"
#include "dwi/tractography/SIFT/proc_mask.h"
#include "dwi/tractography/SIFT/sift.h"
#include "dwi/tractography/SIFT/sifter.h"
using namespace MR;
using namespace App;
using namespace MR::DWI;
using namespace MR::DWI::Tractography;
using namespace MR::DWI::Tractography::Mapping;
using namespace MR::DWI::Tractography::SIFT;
void usage ()
{
AUTHOR = "Robert E. Smith (robert.smith@florey.edu.au)";
SYNOPSIS = "Filter a whole-brain fibre-tracking data set such that the streamline densities match the FOD lobe integrals";
REFERENCES
+ "Smith, R. E.; Tournier, J.-D.; Calamante, F. & Connelly, A. " // Internal
"SIFT: Spherical-deconvolution informed filtering of tractograms. "
"NeuroImage, 2013, 67, 298-312";
ARGUMENTS
+ Argument ("in_tracks", "the input track file").type_tracks_in()
+ Argument ("in_fod", "input image containing the spherical harmonics of the fibre orientation distributions").type_image_in()
+ Argument ("out_tracks", "the output filtered tracks file").type_tracks_out();
OPTIONS
+ Option ("nofilter", "do NOT perform track filtering - just construct the model in order to provide output debugging images")
+ Option ("output_at_counts", "output filtered track files (and optionally debugging images if -output_debug is specified) at specific "
"numbers of remaining streamlines; provide as comma-separated list of integers")
+ Argument ("counts").type_sequence_int()
+ SIFTModelProcMaskOption
+ SIFTModelOption
+ SIFTOutputOption
+ Option ("out_selection", "output a text file containing the binary selection of streamlines")
+ Argument ("path").type_file_out()
+ SIFTTermOption;
}
void run ()
{
auto opt = get_options ("output_debug");
const bool out_debug = opt.size();
auto in_dwi = Image<float>::open (argument[1]);
Math::SH::check (in_dwi);
DWI::Directions::FastLookupSet dirs (1281);
SIFTer sifter (in_dwi, dirs);
if (out_debug) {
sifter.output_proc_mask ("proc_mask.mif");
if (get_options("act").size())
sifter.output_5tt_image ("5tt.mif");
}
sifter.perform_FOD_segmentation (in_dwi);
sifter.scale_FDs_by_GM();
sifter.map_streamlines (argument[0]);
if (out_debug)
sifter.output_all_debug_images ("before");
sifter.remove_excluded_fixels ();
opt = get_options ("nofilter");
if (!opt.size()) {
opt = get_options ("term_number");
if (opt.size())
sifter.set_term_number (int(opt[0][0]));
opt = get_options ("term_ratio");
if (opt.size())
sifter.set_term_ratio (float(opt[0][0]));
opt = get_options ("term_mu");
if (opt.size())
sifter.set_term_mu (float(opt[0][0]));
opt = get_options ("csv");
if (opt.size())
sifter.set_csv_path (opt[0][0]);
opt = get_options ("output_at_counts");
if (opt.size()) {
vector<uint32_t> counts = parse_ints<uint32_t> (opt[0][0]);
sifter.set_regular_outputs (counts, out_debug);
}
sifter.perform_filtering();
if (out_debug)
sifter.output_all_debug_images ("after");
sifter.output_filtered_tracks (argument[0], argument[2]);
opt = get_options ("out_selection");
if (opt.size())
sifter.output_selection (opt[0][0]);
}
opt = get_options ("out_mu");
if (opt.size()) {
File::OFStream out_mu (opt[0][0]);
out_mu << sifter.mu();
}
}
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