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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 "memory.h"
#include "progressbar.h"
#include "algo/threaded_loop.h"
#include "image.h"
#include "math/SH.h"
#include "math/ZSH.h"
using namespace MR;
using namespace App;
void usage ()
{
AUTHOR = "David Raffelt (david.raffelt@florey.edu.au) and J-Donald Tournier (jdtournier@gmail.com)";
SYNOPSIS = "Perform spherical convolution";
DESCRIPTION
+ "Provided with matching pairs of response function and ODF images "
"(containing SH coefficients), perform spherical convolution to provide the "
"corresponding SH coefficients of the signal."
+ "If multiple pairs of inputs are provided, their contributions will be "
"summed into a single output."
+ "If the responses are multi-shell (with one line of coefficients per "
"shell), the output will be a 5-dimensional image, with the SH "
"coefficients of the signal in each shell stored at different indices "
"along the 5th dimension."
+ Math::SH::encoding_description;
DESCRIPTION
+ Math::SH::encoding_description;
ARGUMENTS
+ Argument ("odf response", "pairs of input ODF image and corresponding responses").allow_multiple()
+ Argument ("SH_out", "the output spherical harmonics coefficients image.").type_image_out ();
OPTIONS
+ DataType::options()
+ Stride::Options;
}
using value_type = float;
class SConvFunctor { MEMALIGN(SConvFunctor)
public:
SConvFunctor (const vector<Eigen::MatrixXd>& responses, vector<Image<value_type>>& inputs) :
responses (responses),
inputs (inputs) { }
void operator() (Image<value_type>& output)
{
for (size_t n = 0; n < inputs.size(); ++n) {
assign_pos_of (output, 0, 3).to (inputs[n]);
in = inputs[n].row (3);
for (ssize_t s = 0; s < responses[n].rows(); ++s) {
Math::SH::sconv (out, responses[n].row(s), in);
if (output.ndim() > 4)
output.index(4) = s;
for (ssize_t k = 0; k < out.size(); ++k) {
output.index(3) = k;
output.value() += out[k];
}
}
}
}
protected:
const vector<Eigen::MatrixXd>& responses;
vector<Image<value_type>> inputs;
Eigen::VectorXd in, out;
};
void run()
{
if (!(argument.size() & size_t(1U)))
throw Exception ("unexpected number of arguments");
vector<Image<value_type>> inputs ((argument.size() - 1) / 2);
vector<Eigen::MatrixXd> responses (inputs.size());
size_t lmax = 0;
for (size_t n = 0; n < inputs.size(); ++n) {
inputs[n] = Image<value_type>::open (argument[2*n]);
Math::SH::check (inputs[n]);
if (inputs[n].ndim() > 4 && inputs[n].size(4) > 1)
throw Exception ("input ODF contains more than 4 dimensions");
responses[n] = load_matrix (argument[2*n+1]);
responses[n].conservativeResizeLike (Eigen::MatrixXd::Zero (responses[n].rows(), Math::ZSH::NforL (Math::SH::LforN (inputs[n].size (3)))));
lmax = std::max (Math::ZSH::LforN (responses[n].cols()), lmax);
for (ssize_t k = 0; k < responses[n].rows(); ++k)
responses[n].row(k) = Math::ZSH::ZSH2RH (responses[n].row(k));
if (n) {
if (responses[n].rows() != responses[0].rows())
throw Exception ("number of shells differs between response files");
check_dimensions (inputs[n], inputs[0], 0, 3);
}
}
Header header (inputs[0]);
if (responses[0].rows() > 1) {
header.ndim() = 5;
header.size(4) = responses[0].rows();
}
else
header.ndim() = 4;
header.size(3) = Math::SH::NforL (lmax);
Stride::set_from_command_line (header, Stride::contiguous_along_axis (3, header));
header.datatype() = DataType::from_command_line (DataType::Float32);
auto output = Image<value_type>::create (argument[argument.size()-1], header);
SConvFunctor sconv (responses, inputs);
ThreadedLoop ("performing spherical convolution", inputs[0], 0, 3).run (sconv, output);
}
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