void
GenericReconCartesianGrappaGadget::perform_calib(IsmrmrdReconBit &recon_bit, ReconObjType &recon_obj, size_t e) {
size_t RO = recon_bit.data_.data_.get_size(0);
size_t E1 = recon_bit.data_.data_.get_size(1);
size_t E2 = recon_bit.data_.data_.get_size(2);
hoNDArray<std::complex<float> > &src = recon_obj.ref_calib_;
hoNDArray<std::complex<float> > &dst = recon_obj.ref_calib_dst_;
size_t ref_RO = src.get_size(0);
size_t ref_E1 = src.get_size(1);
size_t ref_E2 = src.get_size(2);
size_t srcCHA = src.get_size(3);
size_t ref_N = src.get_size(4);
size_t ref_S = src.get_size(5);
size_t ref_SLC = src.get_size(6);
size_t dstCHA = dst.get_size(3);
recon_obj.unmixing_coeff_.create(RO, E1, E2, srcCHA, ref_N, ref_S, ref_SLC);
recon_obj.gfactor_.create(RO, E1, E2, 1, ref_N, ref_S, ref_SLC);
Gadgetron::clear(recon_obj.unmixing_coeff_);
Gadgetron::clear(recon_obj.gfactor_);
if (acceFactorE1_[e] <= 1 && acceFactorE2_[e] <= 1) {
Gadgetron::conjugate(recon_obj.coil_map_, recon_obj.unmixing_coeff_);
} else {
// allocate buffer for kernels
size_t kRO = grappa_kSize_RO.value();
size_t kNE1 = grappa_kSize_E1.value();
size_t kNE2 = grappa_kSize_E2.value();
size_t convKRO(1), convKE1(1), convKE2(1);
bool fitItself = this->downstream_coil_compression.value();
if (E2 > 1) {
std::vector<int> kE1, oE1;
std::vector<int> kE2, oE2;
grappa3d_kerPattern(kE1, oE1, kE2, oE2, convKRO, convKE1, convKE2, (size_t) acceFactorE1_[e],
(size_t) acceFactorE2_[e], kRO, kNE1, kNE2, fitItself);
} else {
std::vector<int> kE1, oE1;
Gadgetron::grappa2d_kerPattern(kE1, oE1, convKRO, convKE1, (size_t) acceFactorE1_[e], kRO, kNE1,
fitItself);
recon_obj.kernelIm_.create(RO, E1, 1, srcCHA, dstCHA, ref_N, ref_S, ref_SLC);
}
recon_obj.kernel_.create(convKRO, convKE1, convKE2, srcCHA, dstCHA, ref_N, ref_S, ref_SLC);
Gadgetron::clear(recon_obj.kernel_);
Gadgetron::clear(recon_obj.kernelIm_);
long long num = ref_N * ref_S * ref_SLC;
long long ii;
// only allow this for loop openmp if num>1 and 2D recon
#pragma omp parallel for default(none) private(ii) shared(src, dst, recon_obj, e, num, ref_N, ref_S, ref_RO, ref_E1, ref_E2, RO, E1, E2, dstCHA, srcCHA, convKRO, convKE1, convKE2, kRO, kNE1, kNE2, fitItself) if(num>1)
for (ii = 0; ii < num; ii++) {
size_t slc = ii / (ref_N * ref_S);
size_t s = (ii - slc * ref_N * ref_S) / (ref_N);
size_t n = ii - slc * ref_N * ref_S - s * ref_N;
std::stringstream os;
os << "n" << n << "_s" << s << "_slc" << slc << "_encoding_" << e;
std::string suffix = os.str();
std::complex<float> *pSrc = &(src(0, 0, 0, 0, n, s, slc));
hoNDArray<std::complex<float> > ref_src(ref_RO, ref_E1, ref_E2, srcCHA, pSrc);
std::complex<float> *pDst = &(dst(0, 0, 0, 0, n, s, slc));
hoNDArray<std::complex<float> > ref_dst(ref_RO, ref_E1, ref_E2, dstCHA, pDst);
// -----------------------------------
if (E2 > 1) {
hoNDArray<std::complex<float> > ker(convKRO, convKE1, convKE2, srcCHA, dstCHA,
&(recon_obj.kernel_(0, 0, 0, 0, 0, n, s, slc)));
if (fitItself)
{
Gadgetron::grappa3d_calib_convolution_kernel(ref_src, ref_dst, (size_t)acceFactorE1_[e],
(size_t)acceFactorE2_[e], grappa_reg_lamda.value(),
grappa_calib_over_determine_ratio.value(), kRO, kNE1,
kNE2, ker);
}
else
{
Gadgetron::grappa3d_calib_convolution_kernel(ref_src, ref_src, (size_t)acceFactorE1_[e],
(size_t)acceFactorE2_[e], grappa_reg_lamda.value(),
grappa_calib_over_determine_ratio.value(), kRO, kNE1,
kNE2, ker);
}
//if (!debug_folder_full_path_.empty())
//{
// gt_exporter_.export_array_complex(ker, debug_folder_full_path_ + "convKer3D_" + suffix);
//}
hoNDArray<std::complex<float> > coilMap(RO, E1, E2, dstCHA,
&(recon_obj.coil_map_(0, 0, 0, 0, n, s, slc)));
hoNDArray<std::complex<float> > unmixC(RO, E1, E2, srcCHA,
&(recon_obj.unmixing_coeff_(0, 0, 0, 0, n, s, slc)));
hoNDArray<float> gFactor(RO, E1, E2, 1, &(recon_obj.gfactor_(0, 0, 0, 0, n, s, slc)));
Gadgetron::grappa3d_unmixing_coeff(ker, coilMap, (size_t) acceFactorE1_[e],
(size_t) acceFactorE2_[e], unmixC, gFactor);
//if (!debug_folder_full_path_.empty())
//{
// gt_exporter_.export_array_complex(unmixC, debug_folder_full_path_ + "unmixC_3D_" + suffix);
//}
//if (!debug_folder_full_path_.empty())
//{
// gt_exporter_.export_array(gFactor, debug_folder_full_path_ + "gFactor_3D_" + suffix);
//}
} else {
hoNDArray<std::complex<float> > acsSrc(ref_RO, ref_E1, srcCHA,
const_cast< std::complex<float> *>(ref_src.begin()));
hoNDArray<std::complex<float> > acsDst(ref_RO, ref_E1, dstCHA,
const_cast< std::complex<float> *>(ref_dst.begin()));
hoNDArray<std::complex<float> > convKer(convKRO, convKE1, srcCHA, dstCHA,
&(recon_obj.kernel_(0, 0, 0, 0, 0, n, s, slc)));
hoNDArray<std::complex<float> > kIm(RO, E1, srcCHA, dstCHA,
&(recon_obj.kernelIm_(0, 0, 0, 0, 0, n, s, slc)));
if (fitItself)
{
Gadgetron::grappa2d_calib_convolution_kernel(acsSrc, acsDst, (size_t)acceFactorE1_[e],
grappa_reg_lamda.value(), kRO, kNE1, convKer);
}
else
{
Gadgetron::grappa2d_calib_convolution_kernel(acsSrc, acsSrc, (size_t)acceFactorE1_[e],
grappa_reg_lamda.value(), kRO, kNE1, convKer);
}
Gadgetron::grappa2d_image_domain_kernel(convKer, RO, E1, kIm);
/*if (!debug_folder_full_path_.empty())
{
gt_exporter_.export_array_complex(convKer, debug_folder_full_path_ + "convKer_" + suffix);
}
if (!debug_folder_full_path_.empty())
{
gt_exporter_.export_array_complex(kIm, debug_folder_full_path_ + "kIm_" + suffix);
}*/
hoNDArray<std::complex<float> > coilMap(RO, E1, dstCHA,
&(recon_obj.coil_map_(0, 0, 0, 0, n, s, slc)));
hoNDArray<std::complex<float> > unmixC(RO, E1, srcCHA,
&(recon_obj.unmixing_coeff_(0, 0, 0, 0, n, s, slc)));
hoNDArray<float> gFactor;
Gadgetron::grappa2d_unmixing_coeff(kIm, coilMap, (size_t) acceFactorE1_[e], unmixC, gFactor);
memcpy(&(recon_obj.gfactor_(0, 0, 0, 0, n, s, slc)), gFactor.begin(),
gFactor.get_number_of_bytes());
/*if (!debug_folder_full_path_.empty())
{
gt_exporter_.export_array_complex(unmixC, debug_folder_full_path_ + "unmixC_" + suffix);
}
if (!debug_folder_full_path_.empty())
{
gt_exporter_.export_array(gFactor, debug_folder_full_path_ + "gFactor_" + suffix);
}*/
}
// -----------------------------------
}
}
}
void GenericReconCartesianNonLinearSpirit2DTGadget::perform_nonlinear_spirit_unwrapping(hoNDArray< std::complex<float> >& kspace,
hoNDArray< std::complex<float> >& kerIm, hoNDArray< std::complex<float> >& ref2DT, hoNDArray< std::complex<float> >& coilMap2DT, hoNDArray< std::complex<float> >& res, size_t e)
{
try
{
bool print_iter = this->spirit_print_iter.value();
size_t RO = kspace.get_size(0);
size_t E1 = kspace.get_size(1);
size_t E2 = kspace.get_size(2);
size_t CHA = kspace.get_size(3);
size_t N = kspace.get_size(4);
size_t S = kspace.get_size(5);
size_t SLC = kspace.get_size(6);
size_t ref_N = kerIm.get_size(4);
size_t ref_S = kerIm.get_size(5);
hoNDArray< std::complex<float> > kspaceLinear(kspace);
res = kspace;
// detect whether random sampling is used
bool use_random_sampling = false;
std::vector<long long> sampled_step_size;
long long n, e1;
for (n=0; n<(long long)N; n++)
{
long long prev_sampled_line = -1;
for (e1=0; e1<(long long)E1; e1++)
{
if(std::abs(kspace(RO/2, e1, 0, 0, 0, 0, 0))>0 && std::abs(kspace(RO/2, e1, 0, CHA-1, 0, 0, 0))>0)
{
if(prev_sampled_line>0)
{
sampled_step_size.push_back(e1 - prev_sampled_line);
}
prev_sampled_line = e1;
}
}
}
if(sampled_step_size.size()>4)
{
size_t s;
for (s=2; s<sampled_step_size.size()-1; s++)
{
if(sampled_step_size[s]!=sampled_step_size[s-1])
{
use_random_sampling = true;
break;
}
}
}
if(use_random_sampling)
{
GDEBUG_STREAM("SPIRIT Non linear, random sampling is detected ... ");
}
Gadgetron::GadgetronTimer timer(false);
boost::shared_ptr< hoNDArray< std::complex<float> > > coilMap;
bool hasCoilMap = false;
if (coilMap2DT.get_size(0) == RO && coilMap2DT.get_size(1) == E1 && coilMap2DT.get_size(3)==CHA)
{
if (ref_N < N)
{
coilMap = boost::shared_ptr< hoNDArray< std::complex<float> > >(new hoNDArray< std::complex<float> >(RO, E1, CHA, coilMap2DT.begin()));
}
else
{
coilMap = boost::shared_ptr< hoNDArray< std::complex<float> > >(new hoNDArray< std::complex<float> >(RO, E1, CHA, ref_N, coilMap2DT.begin()));
}
hasCoilMap = true;
}
hoNDArray<float> gFactor;
float gfactorMedian = 0;
float smallest_eigen_value(0);
// -----------------------------------------------------
// estimate gfactor
// -----------------------------------------------------
// mean over N
hoNDArray< std::complex<float> > meanKSpace;
if(calib_mode_[e]==ISMRMRD_interleaved)
{
Gadgetron::compute_averaged_data_N_S(kspace, true, true, true, meanKSpace);
}
else
{
Gadgetron::compute_averaged_data_N_S(ref2DT, true, true, true, meanKSpace);
}
if (!debug_folder_full_path_.empty()) { gt_exporter_.export_array_complex(meanKSpace, debug_folder_full_path_ + "spirit_nl_2DT_meanKSpace"); }
hoNDArray< std::complex<float> > acsSrc(meanKSpace.get_size(0), meanKSpace.get_size(1), CHA, meanKSpace.begin());
hoNDArray< std::complex<float> > acsDst(meanKSpace.get_size(0), meanKSpace.get_size(1), CHA, meanKSpace.begin());
double grappa_reg_lamda = 0.0005;
size_t kRO = 5;
size_t kE1 = 4;
hoNDArray< std::complex<float> > convKer;
hoNDArray< std::complex<float> > kIm(RO, E1, CHA, CHA);
Gadgetron::grappa2d_calib_convolution_kernel(acsSrc, acsDst, (size_t)this->acceFactorE1_[e], grappa_reg_lamda, kRO, kE1, convKer);
Gadgetron::grappa2d_image_domain_kernel(convKer, RO, E1, kIm);
hoNDArray< std::complex<float> > unmixC;
if(hasCoilMap)
{
Gadgetron::grappa2d_unmixing_coeff(kIm, *coilMap, (size_t)acceFactorE1_[e], unmixC, gFactor);
if (!debug_folder_full_path_.empty()) gt_exporter_.export_array(gFactor, debug_folder_full_path_ + "spirit_nl_2DT_gFactor");
hoNDArray<float> gfactorSorted(gFactor);
std::sort(gfactorSorted.begin(), gfactorSorted.begin()+RO*E1);
gfactorMedian = gFactor((RO*E1 / 2));
GDEBUG_STREAM("SPIRIT Non linear, the median gfactor is found to be : " << gfactorMedian);
}
if (!debug_folder_full_path_.empty()) gt_exporter_.export_array_complex(kIm, debug_folder_full_path_ + "spirit_nl_2DT_kIm");
hoNDArray< std::complex<float> > complexIm;
// compute linear solution as the initialization
if(use_random_sampling)
{
if (this->perform_timing.value()) timer.start("SPIRIT Non linear, perform linear spirit recon ... ");
this->perform_spirit_unwrapping(kspace, kerIm, kspaceLinear);
if (this->perform_timing.value()) timer.stop();
}
else
{
if (this->perform_timing.value()) timer.start("SPIRIT Non linear, perform linear recon ... ");
//size_t ref2DT_RO = ref2DT.get_size(0);
//size_t ref2DT_E1 = ref2DT.get_size(1);
//// mean over N
//hoNDArray< std::complex<float> > meanKSpace;
//Gadgetron::sum_over_dimension(ref2DT, meanKSpace, 4);
//if (!debug_folder_full_path_.empty()) { gt_exporter_.export_array_complex(meanKSpace, debug_folder_full_path_ + "spirit_nl_2DT_meanKSpace"); }
//hoNDArray< std::complex<float> > acsSrc(ref2DT_RO, ref2DT_E1, CHA, meanKSpace.begin());
//hoNDArray< std::complex<float> > acsDst(ref2DT_RO, ref2DT_E1, CHA, meanKSpace.begin());
//double grappa_reg_lamda = 0.0005;
//size_t kRO = 5;
//size_t kE1 = 4;
//hoNDArray< std::complex<float> > convKer;
//hoNDArray< std::complex<float> > kIm(RO, E1, CHA, CHA);
//Gadgetron::grappa2d_calib_convolution_kernel(acsSrc, acsDst, (size_t)this->acceFactorE1_[e], grappa_reg_lamda, kRO, kE1, convKer);
//Gadgetron::grappa2d_image_domain_kernel(convKer, RO, E1, kIm);
//if (!debug_folder_full_path_.empty()) gt_exporter_.export_array_complex(kIm, debug_folder_full_path_ + "spirit_nl_2DT_kIm");
Gadgetron::hoNDFFT<float>::instance()->ifft2c(kspace, complex_im_recon_buf_);
if (!debug_folder_full_path_.empty()) gt_exporter_.export_array_complex(complex_im_recon_buf_, debug_folder_full_path_ + "spirit_nl_2DT_aliasedImage");
hoNDArray< std::complex<float> > resKSpace(RO, E1, CHA, N);
hoNDArray< std::complex<float> > aliasedImage(RO, E1, CHA, N, complex_im_recon_buf_.begin());
Gadgetron::grappa2d_image_domain_unwrapping_aliased_image(aliasedImage, kIm, resKSpace);
if (!debug_folder_full_path_.empty()) gt_exporter_.export_array_complex(resKSpace, debug_folder_full_path_ + "spirit_nl_2DT_linearImage");
Gadgetron::hoNDFFT<float>::instance()->fft2c(resKSpace);
memcpy(kspaceLinear.begin(), resKSpace.begin(), resKSpace.get_number_of_bytes());
Gadgetron::apply_unmix_coeff_aliased_image(aliasedImage, unmixC, complexIm);
if (this->perform_timing.value()) timer.stop();
}
if (!debug_folder_full_path_.empty()) gt_exporter_.export_array_complex(kspaceLinear, debug_folder_full_path_ + "spirit_nl_2DT_kspaceLinear");
if(hasCoilMap)
{
if(N>=spirit_reg_minimal_num_images_for_noise_floor.value())
{
// estimate the noise level
if(use_random_sampling)
{
Gadgetron::hoNDFFT<float>::instance()->ifft2c(kspaceLinear, complex_im_recon_buf_);
hoNDArray< std::complex<float> > complexLinearImage(RO, E1, CHA, N, complex_im_recon_buf_.begin());
Gadgetron::coil_combine(complexLinearImage, *coilMap, 2, complexIm);
}
if (!debug_folder_full_path_.empty()) gt_exporter_.export_array_complex(complexIm, debug_folder_full_path_ + "spirit_nl_2DT_linearImage_complexIm");
// if N is sufficiently large, we can estimate the noise floor by the smallest eigen value
hoMatrix< std::complex<float> > data;
data.createMatrix(RO*E1, N, complexIm.begin(), false);
hoNDArray< std::complex<float> > eigenVectors, eigenValues, eigenVectorsPruned;
// compute eigen
hoNDKLT< std::complex<float> > klt;
klt.prepare(data, (size_t)1, (size_t)0);
klt.eigen_value(eigenValues);
if (this->verbose.value())
{
GDEBUG_STREAM("SPIRIT Non linear, computes eigen values for all 2D kspaces ... ");
eigenValues.print(std::cout);
for (size_t i = 0; i<eigenValues.get_size(0); i++)
{
GDEBUG_STREAM(i << " = " << eigenValues(i));
}
}
smallest_eigen_value = std::sqrt( std::abs(eigenValues(N - 1).real()) / (RO*E1) );
GDEBUG_STREAM("SPIRIT Non linear, the smallest eigen value is : " << smallest_eigen_value);
}
}
// perform nonlinear reconstruction
{
boost::shared_ptr<hoNDArray< std::complex<float> > > ker(new hoNDArray< std::complex<float> >(RO, E1, CHA, CHA, ref_N, kerIm.begin()));
boost::shared_ptr<hoNDArray< std::complex<float> > > acq(new hoNDArray< std::complex<float> >(RO, E1, CHA, N, kspace.begin()));
hoNDArray< std::complex<float> > kspaceInitial(RO, E1, CHA, N, kspaceLinear.begin());
hoNDArray< std::complex<float> > res2DT(RO, E1, CHA, N, res.begin());
if (this->spirit_data_fidelity_lamda.value() > 0)
{
GDEBUG_STREAM("Start the NL SPIRIT data fidelity iteration - regularization strength : " << this->spirit_image_reg_lamda.value()
<< " - number of iteration : " << this->spirit_nl_iter_max.value()
<< " - proximity across cha : " << this->spirit_reg_proximity_across_cha.value()
<< " - redundant dimension weighting ratio : " << this->spirit_reg_N_weighting_ratio.value()
<< " - using coil sen map : " << this->spirit_reg_use_coil_sen_map.value()
<< " - iter thres : " << this->spirit_nl_iter_thres.value()
<< " - wavelet name : " << this->spirit_reg_name.value()
);
typedef hoGdSolver< hoNDArray< std::complex<float> >, hoWavelet2DTOperator< std::complex<float> > > SolverType;
SolverType solver;
solver.iterations_ = this->spirit_nl_iter_max.value();
solver.set_output_mode(this->spirit_print_iter.value() ? SolverType::OUTPUT_VERBOSE : SolverType::OUTPUT_SILENT);
solver.grad_thres_ = this->spirit_nl_iter_thres.value();
if(spirit_reg_estimate_noise_floor.value() && std::abs(smallest_eigen_value)>0)
{
solver.scale_factor_ = smallest_eigen_value;
solver.proximal_strength_ratio_ = this->spirit_image_reg_lamda.value() * gfactorMedian;
GDEBUG_STREAM("SPIRIT Non linear, eigen value is used to derive the regularization strength : " << solver.proximal_strength_ratio_ << " - smallest eigen value : " << solver.scale_factor_);
}
else
{
solver.proximal_strength_ratio_ = this->spirit_image_reg_lamda.value();
}
boost::shared_ptr< hoNDArray< std::complex<float> > > x0 = boost::make_shared< hoNDArray< std::complex<float> > >(kspaceInitial);
solver.set_x0(x0);
// parallel imaging term
std::vector<size_t> dims;
acq->get_dimensions(dims);
hoSPIRIT2DTDataFidelityOperator< std::complex<float> > spirit(&dims);
spirit.set_forward_kernel(*ker, false);
spirit.set_acquired_points(*acq);
// image reg term
hoWavelet2DTOperator< std::complex<float> > wav3DOperator(&dims);
wav3DOperator.set_acquired_points(*acq);
wav3DOperator.scale_factor_first_dimension_ = this->spirit_reg_RO_weighting_ratio.value();
wav3DOperator.scale_factor_second_dimension_ = this->spirit_reg_E1_weighting_ratio.value();
wav3DOperator.scale_factor_third_dimension_ = this->spirit_reg_N_weighting_ratio.value();
wav3DOperator.with_approx_coeff_ = !this->spirit_reg_keep_approx_coeff.value();
wav3DOperator.change_coeffcients_third_dimension_boundary_ = !this->spirit_reg_keep_redundant_dimension_coeff.value();
wav3DOperator.proximity_across_cha_ = this->spirit_reg_proximity_across_cha.value();
wav3DOperator.no_null_space_ = true;
wav3DOperator.input_in_kspace_ = true;
wav3DOperator.select_wavelet(this->spirit_reg_name.value());
if (this->spirit_reg_use_coil_sen_map.value() && hasCoilMap)
{
wav3DOperator.coil_map_ = *coilMap;
}
// set operators
solver.oper_system_ = &spirit;
solver.oper_reg_ = &wav3DOperator;
if (this->perform_timing.value()) timer.start("NonLinear SPIRIT solver for 2DT with data fidelity ... ");
solver.solve(*acq, res2DT);
if (this->perform_timing.value()) timer.stop();
if (!debug_folder_full_path_.empty()) gt_exporter_.export_array_complex(res2DT, debug_folder_full_path_ + "spirit_nl_2DT_data_fidelity_res");
}
else
{
GDEBUG_STREAM("Start the NL SPIRIT iteration with regularization strength : "<< this->spirit_image_reg_lamda.value()
<< " - number of iteration : " << this->spirit_nl_iter_max.value()
<< " - proximity across cha : " << this->spirit_reg_proximity_across_cha.value()
<< " - redundant dimension weighting ratio : " << this->spirit_reg_N_weighting_ratio.value()
<< " - using coil sen map : " << this->spirit_reg_use_coil_sen_map.value()
<< " - iter thres : " << this->spirit_nl_iter_thres.value()
<< " - wavelet name : " << this->spirit_reg_name.value()
);
typedef hoGdSolver< hoNDArray< std::complex<float> >, hoWavelet2DTOperator< std::complex<float> > > SolverType;
SolverType solver;
solver.iterations_ = this->spirit_nl_iter_max.value();
solver.set_output_mode(this->spirit_print_iter.value() ? SolverType::OUTPUT_VERBOSE : SolverType::OUTPUT_SILENT);
solver.grad_thres_ = this->spirit_nl_iter_thres.value();
if(spirit_reg_estimate_noise_floor.value() && std::abs(smallest_eigen_value)>0)
{
solver.scale_factor_ = smallest_eigen_value;
solver.proximal_strength_ratio_ = this->spirit_image_reg_lamda.value() * gfactorMedian;
GDEBUG_STREAM("SPIRIT Non linear, eigen value is used to derive the regularization strength : " << solver.proximal_strength_ratio_ << " - smallest eigen value : " << solver.scale_factor_);
}
else
{
solver.proximal_strength_ratio_ = this->spirit_image_reg_lamda.value();
}
boost::shared_ptr< hoNDArray< std::complex<float> > > x0 = boost::make_shared< hoNDArray< std::complex<float> > >(kspaceInitial);
solver.set_x0(x0);
// parallel imaging term
std::vector<size_t> dims;
acq->get_dimensions(dims);
hoSPIRIT2DTOperator< std::complex<float> > spirit(&dims);
spirit.set_forward_kernel(*ker, false);
spirit.set_acquired_points(*acq);
spirit.no_null_space_ = true;
spirit.use_non_centered_fft_ = false;
// image reg term
std::vector<size_t> dim;
acq->get_dimensions(dim);
hoWavelet2DTOperator< std::complex<float> > wav3DOperator(&dim);
wav3DOperator.set_acquired_points(*acq);
wav3DOperator.scale_factor_first_dimension_ = this->spirit_reg_RO_weighting_ratio.value();
wav3DOperator.scale_factor_second_dimension_ = this->spirit_reg_E1_weighting_ratio.value();
wav3DOperator.scale_factor_third_dimension_ = this->spirit_reg_N_weighting_ratio.value();
wav3DOperator.with_approx_coeff_ = !this->spirit_reg_keep_approx_coeff.value();
wav3DOperator.change_coeffcients_third_dimension_boundary_ = !this->spirit_reg_keep_redundant_dimension_coeff.value();
wav3DOperator.proximity_across_cha_ = this->spirit_reg_proximity_across_cha.value();
wav3DOperator.no_null_space_ = true;
wav3DOperator.input_in_kspace_ = true;
wav3DOperator.select_wavelet(this->spirit_reg_name.value());
if (this->spirit_reg_use_coil_sen_map.value() && hasCoilMap)
{
wav3DOperator.coil_map_ = *coilMap;
}
// set operators
solver.oper_system_ = &spirit;
solver.oper_reg_ = &wav3DOperator;
// set call back
solverCallBack cb;
cb.solver_ = &solver;
solver.call_back_ = &cb;
hoNDArray< std::complex<float> > b(kspaceInitial);
Gadgetron::clear(b);
if (this->perform_timing.value()) timer.start("NonLinear SPIRIT solver for 2DT ... ");
solver.solve(b, res2DT);
if (this->perform_timing.value()) timer.stop();
if (!debug_folder_full_path_.empty()) gt_exporter_.export_array_complex(res2DT, debug_folder_full_path_ + "spirit_nl_2DT_res");
spirit.restore_acquired_kspace(kspace, res2DT);
if (!debug_folder_full_path_.empty()) gt_exporter_.export_array_complex(res2DT, debug_folder_full_path_ + "spirit_nl_2DT_res_restored");
}
}
}
catch (...)
{
GADGET_THROW("Errors happened in GenericReconCartesianNonLinearSpirit2DTGadget::perform_nonlinear_spirit_unwrapping(...) ... ");
}
}
void MultiChannelCartesianGrappaReconGadget::perform_calib(IsmrmrdReconBit& recon_bit, ReconObjType& recon_obj, size_t e)
{
try
{
size_t RO = recon_bit.data_.data_.get_size(0);
size_t E1 = recon_bit.data_.data_.get_size(1);
size_t E2 = recon_bit.data_.data_.get_size(2);
hoNDArray< std::complex<float> >& src = recon_obj.ref_calib_;
hoNDArray< std::complex<float> >& dst = recon_obj.ref_calib_;
size_t ref_RO = src.get_size(0);
size_t ref_E1 = src.get_size(1);
size_t ref_E2 = src.get_size(2);
size_t srcCHA = src.get_size(3);
size_t ref_N = src.get_size(4);
size_t ref_S = src.get_size(5);
size_t ref_SLC = src.get_size(6);
size_t dstCHA = dst.get_size(3);
recon_obj.unmixing_coeff_.create(RO, E1, E2, srcCHA, ref_N, ref_S, ref_SLC);
recon_obj.gfactor_.create(RO, E1, E2, 1, ref_N, ref_S, ref_SLC);
Gadgetron::clear(recon_obj.unmixing_coeff_);
Gadgetron::clear(recon_obj.gfactor_);
if (acceFactorE1_[e] <= 1 && acceFactorE2_[e] <= 1)
{
Gadgetron::conjugate(recon_obj.coil_map_, recon_obj.unmixing_coeff_);
}
else
{
// allocate buffer for kernels
size_t kRO = grappa_kSize_RO.value();
size_t kNE1 = grappa_kSize_E1.value();
size_t kNE2 = grappa_kSize_E2.value();
size_t convKRO(1), convKE1(1), convKE2(1);
if (E2 > 1)
{
std::vector<int> kE1, oE1;
std::vector<int> kE2, oE2;
bool fitItself = true;
grappa3d_kerPattern(kE1, oE1, kE2, oE2, convKRO, convKE1, convKE2, (size_t)acceFactorE1_[e], (size_t)acceFactorE2_[e], kRO, kNE1, kNE2, fitItself);
}
else
{
std::vector<int> kE1, oE1;
bool fitItself = true;
Gadgetron::grappa2d_kerPattern(kE1, oE1, convKRO, convKE1, (size_t)acceFactorE1_[e], kRO, kNE1, fitItself);
recon_obj.kernelIm_.create(RO, E1, 1, srcCHA, dstCHA, ref_N, ref_S, ref_SLC);
}
recon_obj.kernel_.create(convKRO, convKE1, convKE2, srcCHA, dstCHA, ref_N, ref_S, ref_SLC);
Gadgetron::clear(recon_obj.kernel_);
Gadgetron::clear(recon_obj.kernelIm_);
long long num = ref_N*ref_S*ref_SLC;
long long ii;
#pragma omp parallel for default(none) private(ii) shared(src, dst, recon_obj, e, num, ref_N, ref_S, ref_RO, ref_E1, ref_E2, RO, E1, E2, dstCHA, srcCHA, convKRO, convKE1, convKE2, kRO, kNE1, kNE2) if(num>1)
for (ii = 0; ii < num; ii++)
{
size_t slc = ii / (ref_N*ref_S);
size_t s = (ii - slc*ref_N*ref_S) / (ref_N);
size_t n = ii - slc*ref_N*ref_S - s*ref_N;
std::stringstream os;
os << "n" << n << "_s" << s << "_slc" << slc << "_encoding_" << e;
std::string suffix = os.str();
std::complex<float>* pSrc = &(src(0, 0, 0, 0, n, s, slc));
hoNDArray< std::complex<float> > ref_src(ref_RO, ref_E1, ref_E2, srcCHA, pSrc);
std::complex<float>* pDst = &(dst(0, 0, 0, 0, n, s, slc));
hoNDArray< std::complex<float> > ref_dst(ref_RO, ref_E1, ref_E2, dstCHA, pDst);
// -----------------------------------
if (E2 > 1)
{
hoNDArray< std::complex<float> > ker(convKRO, convKE1, convKE2, srcCHA, dstCHA, &(recon_obj.kernel_(0, 0, 0, 0, 0, n, s, slc)));
Gadgetron::grappa3d_calib_convolution_kernel(ref_src, ref_dst, (size_t)acceFactorE1_[e], (size_t)acceFactorE2_[e], grappa_reg_lamda.value(), grappa_calib_over_determine_ratio.value(), kRO, kNE1, kNE2, ker);
hoNDArray< std::complex<float> > coilMap(RO, E1, E2, dstCHA, &(recon_obj.coil_map_(0, 0, 0, 0, n, s, slc)));
hoNDArray< std::complex<float> > unmixC(RO, E1, E2, srcCHA, &(recon_obj.unmixing_coeff_(0, 0, 0, 0, n, s, slc)));
hoNDArray<float> gFactor(RO, E1, E2, 1, &(recon_obj.gfactor_(0, 0, 0, 0, n, s, slc)));
Gadgetron::grappa3d_unmixing_coeff(ker, coilMap, (size_t)acceFactorE1_[e], (size_t)acceFactorE2_[e], unmixC, gFactor);
}
else
{
hoNDArray< std::complex<float> > acsSrc(ref_RO, ref_E1, srcCHA, const_cast< std::complex<float>*>(ref_src.begin()));
hoNDArray< std::complex<float> > acsDst(ref_RO, ref_E1, dstCHA, const_cast< std::complex<float>*>(ref_dst.begin()));
hoNDArray< std::complex<float> > convKer(convKRO, convKE1, srcCHA, dstCHA, &(recon_obj.kernel_(0, 0, 0, 0, 0, n, s, slc)));
hoNDArray< std::complex<float> > kIm(RO, E1, srcCHA, dstCHA, &(recon_obj.kernelIm_(0, 0, 0, 0, 0, n, s, slc)));
Gadgetron::grappa2d_calib_convolution_kernel(acsSrc, acsDst, (size_t)acceFactorE1_[e], grappa_reg_lamda.value(), kRO, kNE1, convKer);
Gadgetron::grappa2d_image_domain_kernel(convKer, RO, E1, kIm);
hoNDArray< std::complex<float> > coilMap(RO, E1, dstCHA, &(recon_obj.coil_map_(0, 0, 0, 0, n, s, slc)));
hoNDArray< std::complex<float> > unmixC(RO, E1, srcCHA, &(recon_obj.unmixing_coeff_(0, 0, 0, 0, n, s, slc)));
hoNDArray<float> gFactor;
Gadgetron::grappa2d_unmixing_coeff(kIm, coilMap, (size_t)acceFactorE1_[e], unmixC, gFactor);
memcpy(&(recon_obj.gfactor_(0, 0, 0, 0, n, s, slc)), gFactor.begin(), gFactor.get_number_of_bytes());
}
// -----------------------------------
}
}
}
catch (...)
{
GADGET_THROW("Errors happened in MultiChannelCartesianGrappaReconGadget::perform_calib(...) ... ");
}
}