void run ()
{
Image::Buffer<value_type> dwi_data (argument[0]);
if (dwi_data.ndim() != 4)
throw Exception ("dwi image should contain 4 dimensions");
Math::Matrix<value_type> grad = DWI::get_valid_DW_scheme<value_type> (dwi_data);
DWI::Shells shells (grad);
// Keep the b=0 shell (may be used for normalisation), but force single non-zero shell
shells.select_shells (true, true);
Math::Matrix<value_type> DW_dirs = DWI::gen_direction_matrix (grad, shells.largest().get_volumes());
Options opt = get_options ("lmax");
lmax = opt.size() ? opt[0][0] : Math::SH::LforN (shells.largest().count());
INFO ("calculating even spherical harmonic components up to order " + str (lmax));
Math::Matrix<value_type> HR_dirs;
Math::Matrix<value_type> HR_SHT;
opt = get_options ("normalise");
if (opt.size()) {
normalise = true;
opt = get_options ("directions");
if (opt.size())
HR_dirs.load (opt[0][0]);
else
DWI::Directions::electrostatic_repulsion_300 (HR_dirs);
Math::SH::init_transform (HR_SHT, HR_dirs, lmax);
}
// set Lmax
int i;
for (i = 0; Math::SH::NforL(i) < shells.largest().count(); i += 2);
i -= 2;
if (lmax > i) {
WARN ("not enough data for SH order " + str(lmax) + ", falling back to " + str(i));
lmax = i;
}
INFO("setting maximum even spherical harmonic order to " + str(lmax));
// Setup response function
int num_RH = (lmax + 2)/2;
Math::Vector<value_type> sigs(num_RH);
std::vector<value_type> AL (lmax+1);
Math::Legendre::Plm_sph<value_type>(&AL[0], lmax, 0, 0);
for (int l = 0; l <= lmax; l += 2) sigs[l/2] = AL[l];
Math::Vector<value_type> response(num_RH);
Math::SH::SH2RH(response, sigs);
opt = get_options ("filter");
Math::Vector<value_type> filter;
if (opt.size()) {
filter.load (opt[0][0]);
if (filter.size() <= response.size())
throw Exception ("not enough filter coefficients supplied for lmax" + str(lmax));
for (int i = 0; i <= lmax/2; i++) response[i] *= filter[i];
INFO ("using initial filter coefficients: " + str (filter));
}
Math::SH::Transform<value_type> FRT_SHT(DW_dirs, lmax);
FRT_SHT.set_filter(response);
Image::Header qbi_header (dwi_data);
qbi_header.dim(3) = Math::SH::NforL (lmax);
qbi_header.datatype() = DataType::Float32;
Image::Stride::set (qbi_header, Image::Stride::contiguous_along_axis (3, dwi_data));
Image::Buffer<value_type> qbi_data (argument[1], qbi_header);
opt = get_options ("mask");
if (opt.size()) {
Image::Buffer<bool> mask_data (opt[0][0]);
Image::ThreadedLoop ("estimating dODFs using Q-ball imaging...", dwi_data, 0, 3)
.run (DWI2QBI (FRT_SHT.mat_A2SH(), HR_SHT, shells), mask_data.voxel(), dwi_data.voxel(), qbi_data.voxel());
}
else
Image::ThreadedLoop ("estimating dODFs using Q-ball imaging...", dwi_data, 0, 3)
.run (DWI2QBI (FRT_SHT.mat_A2SH(), HR_SHT, shells), dwi_data.voxel(), qbi_data.voxel());
}
void run ()
{
Image::Header H (argument[0]);
Image::Info info (H);
info.set_ndim (3);
Image::BufferScratch<bool> mask (info);
auto v_mask = mask.voxel();
std::string mask_path;
Options opt = get_options ("mask");
if (opt.size()) {
mask_path = std::string(opt[0][0]);
Image::Buffer<bool> in (mask_path);
if (!Image::dimensions_match (H, in, 0, 3))
throw Exception ("Input mask image does not match DWI");
if (!(in.ndim() == 3 || (in.ndim() == 4 && in.dim(3) == 1)))
throw Exception ("Input mask image must be a 3D image");
auto v_in = in.voxel();
Image::copy (v_in, v_mask, 0, 3);
} else {
for (auto l = Image::LoopInOrder (v_mask) (v_mask); l; ++l)
v_mask.value() = true;
}
DWI::CSDeconv<float>::Shared shared (H);
const size_t lmax = DWI::lmax_for_directions (shared.DW_dirs);
if (lmax < 4)
throw Exception ("Cannot run dwi2response with lmax less than 4");
shared.lmax = lmax;
Image::BufferPreload<float> dwi (H, Image::Stride::contiguous_along_axis (3));
DWI::Directions::Set directions (1281);
Math::Vector<float> response (lmax/2+1);
response.zero();
{
// Initialise response function
// Use lmax = 2, get the DWI intensity mean and standard deviation within the mask and
// use these as the first two coefficients
auto v_dwi = dwi.voxel();
double sum = 0.0, sq_sum = 0.0;
size_t count = 0;
Image::LoopInOrder loop (dwi, "initialising response function... ", 0, 3);
for (auto l = loop (v_dwi, v_mask); l; ++l) {
if (v_mask.value()) {
for (size_t volume_index = 0; volume_index != shared.dwis.size(); ++volume_index) {
v_dwi[3] = shared.dwis[volume_index];
const float value = v_dwi.value();
sum += value;
sq_sum += Math::pow2 (value);
++count;
}
}
}
response[0] = sum / double (count);
response[1] = - 0.5 * std::sqrt ((sq_sum / double(count)) - Math::pow2 (response[0]));
// Account for scaling in SH basis
response *= std::sqrt (4.0 * Math::pi);
}
INFO ("Initial response function is [" + str(response, 2) + "]");
// Algorithm termination options
opt = get_options ("max_iters");
const size_t max_iters = opt.size() ? int(opt[0][0]) : DWI2RESPONSE_DEFAULT_MAX_ITERS;
opt = get_options ("max_change");
const float max_change = 0.01 * (opt.size() ? float(opt[0][0]) : DWI2RESPONSE_DEFAULT_MAX_CHANGE);
// Should all voxels (potentially within a user-specified mask) be tested at every iteration?
opt = get_options ("test_all");
const bool reset_mask = opt.size();
// Single-fibre voxel selection options
opt = get_options ("volume_ratio");
const float volume_ratio = opt.size() ? float(opt[0][0]) : DWI2RESPONSE_DEFAULT_VOLUME_RATIO;
opt = get_options ("dispersion_multiplier");
const float dispersion_multiplier = opt.size() ? float(opt[0][0]) : DWI2RESPONSE_DEFAULT_DISPERSION_MULTIPLIER;
opt = get_options ("integral_multiplier");
const float integral_multiplier = opt.size() ? float(opt[0][0]) : DWI2RESPONSE_DEFAULT_INTEGRAL_STDEV_MULTIPLIER;
SFThresholds thresholds (volume_ratio); // Only threshold the lobe volume ratio for now; other two are not yet used
size_t total_iter = 0;
bool first_pass = true;
size_t prev_sf_count = 0;
{
bool iterate = true;
size_t iter = 0;
ProgressBar progress ("optimising response function... ");
do {
++iter;
{
MR::LogLevelLatch latch (0);
shared.set_response (response);
shared.init();
}
++progress;
if (reset_mask) {
if (mask_path.size()) {
Image::Buffer<bool> in (mask_path);
auto v_in = in.voxel();
Image::copy (v_in, v_mask, 0, 3);
} else {
for (auto l = Image::LoopInOrder(v_mask) (v_mask); l; ++l)
v_mask.value() = true;
}
++progress;
}
std::vector<FODSegResult> seg_results;
{
FODCalcAndSeg processor (dwi, mask, shared, directions, lmax, seg_results);
Image::ThreadedLoop loop (mask, 0, 3);
loop.run (processor);
}
++progress;
if (!first_pass)
thresholds.update (seg_results, dispersion_multiplier, integral_multiplier, iter);
++progress;
Response output (lmax);
mask.zero();
{
SFSelector selector (seg_results, thresholds, mask);
ResponseEstimator estimator (dwi, shared, lmax, output);
Thread::run_queue (selector, FODSegResult(), Thread::multi (estimator));
}
if (!output.get_count())
throw Exception ("Cannot estimate response function; all voxels have been excluded from selection");
const Math::Vector<float> new_response = output.result();
const size_t sf_count = output.get_count();
++progress;
if (App::log_level >= 2)
std::cerr << "\n";
INFO ("Iteration " + str(iter) + ", " + str(sf_count) + " SF voxels, new response function: [" + str(new_response, 2) + "]");
if (sf_count == prev_sf_count) {
INFO ("terminating due to convergence of single-fibre voxel selection");
iterate = false;
}
if (iter == max_iters) {
INFO ("terminating due to completing maximum number of iterations");
iterate = false;
}
bool rf_changed = false;
for (size_t i = 0; i != response.size(); ++i) {
if (std::abs ((new_response[i] - response[i]) / new_response[i]) > max_change)
rf_changed = true;
}
if (!rf_changed) {
INFO ("terminating due to negligible changes in the response function coefficients");
iterate = false;
}
if (!iterate && first_pass) {
iterate = true;
first_pass = false;
INFO ("commencing second-pass of response function estimation");
total_iter = iter;
iter = 0;
}
response = new_response;
prev_sf_count = sf_count;
//v_mask.save ("mask_pass_" + str(first_pass?1:2) + "_iter_" + str(iter) + ".mif");
} while (iterate);
total_iter += iter;
}
CONSOLE ("final response function: [" + str(response, 2) + "] (reached after " + str(total_iter) + " iterations using " + str(prev_sf_count) + " voxels)");
response.save (argument[1]);
opt = get_options ("sf");
if (opt.size())
v_mask.save (std::string (opt[0][0]));
}