boost::shared_ptr<cuNDArray<float_complext> > gpuBufferSensePrepGadget::reconstruct_regularization(
cuNDArray<float_complext>* data, cuNDArray<floatd2>* traj, cuNDArray<float>* dcw, size_t ncoils ) {
if (dcw) { //We have density compensation, so we can get away with gridding
cuNFFT_plan<float,2> plan(from_std_vector<size_t,2>(image_dims_recon_),image_dims_recon_os_,kernel_width_);
std::vector<size_t> csm_dims = image_dims_recon_;
csm_dims.push_back(ncoils);
auto result = new cuNDArray<float_complext>(csm_dims);
GDEBUG("Coils %i \n\n",ncoils);
std::vector<size_t> flat_dims = {traj->get_number_of_elements()};
cuNDArray<floatd2> flat_traj(flat_dims,traj->get_data_ptr());
GDEBUG("traj: %i data %i\n",traj->get_number_of_elements(),data->get_number_of_elements());
GDEBUG("Preprocessing\n\n");
plan.preprocess(&flat_traj,cuNFFT_plan<float,2>::NFFT_PREP_NC2C);
GDEBUG("Computing\n\n");
plan.compute(data,result,dcw,cuNFFT_plan<float,2>::NFFT_BACKWARDS_NC2C);
return boost::shared_ptr<cuNDArray<float_complext>>(result);
} else { //No density compensation, we have to do iterative reconstruction.
std::vector<size_t> csm_dims = image_dims_recon_;
csm_dims.push_back(ncoils);
auto E = boost::make_shared<cuNFFTOperator<float,2>>();
E->setup(from_std_vector<size_t,2>(image_dims_recon_),image_dims_recon_os_,kernel_width_);
std::vector<size_t> flat_dims = {traj->get_number_of_elements()};
cuNDArray<floatd2> flat_traj(flat_dims,traj->get_data_ptr());
E->set_domain_dimensions(&csm_dims);
cuCgSolver<float_complext> solver;
solver.set_max_iterations(0);
solver.set_encoding_operator(E);
solver.set_output_mode(cuCgSolver<float_complext>::OUTPUT_VERBOSE);
E->set_codomain_dimensions(data->get_dimensions().get());
E->preprocess(&flat_traj);
auto res = solver.solve(data);
return res;
}
}
boost::shared_ptr<cuNDArray<float_complext> > gpuCSICoilEstimationGadget::calculate_CSM(
cuNDArray<float_complext>* data, cuNDArray<floatd2>* traj, cuNDArray<float>* dcw ) {
if (dcw) { //We have density compensation, so we can get away with gridding
cuNFFT_plan<float,2> plan(from_std_vector<size_t,2>(img_size),from_std_vector<size_t,2>(img_size)*size_t(2),kernel_width_);
std::vector<size_t> csm_dims = img_size;
csm_dims.push_back(coils);
cuNDArray<float_complext> tmp(csm_dims);
GDEBUG("Coils %i \n\n",tmp.get_size(2));
std::vector<size_t> flat_dims = {traj->get_number_of_elements()};
cuNDArray<floatd2> flat_traj(flat_dims,traj->get_data_ptr());
std::vector<size_t> spiral_dims{data->get_size(0),data->get_size(1)}; //Trajectories, coils
cuNDArray<complext<float>> second_spiral(spiral_dims,data->get_data_ptr()+spiral_dims[0]*spiral_dims[1]*0);
std::vector<size_t> spiral_traj_dims{spiral_dims[0]};
cuNDArray<floatd2> spiral_traj(spiral_traj_dims,traj->get_data_ptr()+spiral_dims[0]*0);
cuNDArray<float> spiral_dcw(spiral_traj_dims,dcw->get_data_ptr()+spiral_dims[0]*0);
GDEBUG("Preprocessing\n\n");
plan.preprocess(&spiral_traj,cuNFFT_plan<float,2>::NFFT_PREP_NC2C);
GDEBUG("Computing\n\n");
plan.compute(&second_spiral,&tmp,&spiral_dcw,cuNFFT_plan<float,2>::NFFT_BACKWARDS_NC2C);
auto tmp_abs = abs(&tmp);
return estimate_b1_map<float,2>(&tmp);
} else { //No density compensation, we have to do iterative reconstruction.
std::vector<size_t> csm_dims = img_size;
csm_dims.push_back(coils);
auto E = boost::make_shared<cuNFFTOperator<float,2>>();
E->setup(from_std_vector<size_t,2>(img_size),from_std_vector<size_t,2>(img_size)*size_t(2),kernel_width_);
std::vector<size_t> flat_dims = {traj->get_number_of_elements()};
cuNDArray<floatd2> flat_traj(flat_dims,traj->get_data_ptr());
E->set_domain_dimensions(&csm_dims);
cuCgSolver<float_complext> solver;
solver.set_max_iterations(20);
solver.set_encoding_operator(E);
std::vector<size_t> spiral_dims{data->get_size(0),data->get_size(1)}; //Trajectories, coils
cuNDArray<complext<float>> second_spiral(spiral_dims,data->get_data_ptr()+spiral_dims[0]*spiral_dims[1]*0);
E->set_codomain_dimensions(&spiral_dims);
std::vector<size_t> spiral_traj_dims{spiral_dims[0]};
cuNDArray<floatd2> spiral_traj(spiral_traj_dims,traj->get_data_ptr()+spiral_dims[0]*0);
E->preprocess(&spiral_traj);
auto tmp = solver.solve(&second_spiral);
auto tmp_abs = abs(tmp.get());
auto res = estimate_b1_map<float,2>(tmp.get());
//fill(res.get(),float_complext(1,0));
//auto res= boost::make_shared<cuNDArray<float_complext>>(csm_dims);
//fill(res.get(),float_complext(1,0));
return res;
}
}
