const char* reconstruction(ImageModel* image_model,
unsigned int method_id,
const float* param_values,
bool check_b_table,
unsigned int thread_count)
{
static std::string output_name;
try
{
image_model->voxel.recon_report.clear();
image_model->voxel.recon_report.str("");
image_model->voxel.param = param_values;
std::ostringstream out;
if(method_id == 1) // DTI
{
image_model->voxel.need_odf = 0;
image_model->voxel.output_jacobian = 0;
image_model->voxel.output_mapping = 0;
image_model->voxel.output_rdi = 0;
image_model->voxel.scheme_balance = 0;
image_model->voxel.half_sphere = 0;
image_model->voxel.odf_deconvolusion = 0;
image_model->voxel.odf_decomposition = 0;
}
else
{
out << ".odf" << image_model->voxel.ti.fold;// odf_order
out << ".f" << image_model->voxel.max_fiber_number;
if (image_model->voxel.need_odf)
out << "rec";
if (image_model->voxel.scheme_balance)
out << ".bal";
if (image_model->voxel.half_sphere)
out << ".hs";
if (image_model->voxel.csf_calibration && method_id == 4)
out << ".csfc";
else
image_model->voxel.csf_calibration = false;
if (image_model->voxel.odf_deconvolusion)
{
out << ".de" << param_values[2];
if(image_model->voxel.odf_xyz[0] != 0 ||
image_model->voxel.odf_xyz[1] != 0 ||
image_model->voxel.odf_xyz[2] != 0)
out << ".at_" << image_model->voxel.odf_xyz[0]
<< "_" << image_model->voxel.odf_xyz[1]
<< "_" << image_model->voxel.odf_xyz[2];
}
if (image_model->voxel.odf_decomposition)
{
out << ".dec" << param_values[3] << "m" << (int)param_values[4];
if(image_model->voxel.odf_xyz[0] != 0 ||
image_model->voxel.odf_xyz[1] != 0 ||
image_model->voxel.odf_xyz[2] != 0)
out << ".at_" << image_model->voxel.odf_xyz[0]
<< "_" << image_model->voxel.odf_xyz[1]
<< "_" << image_model->voxel.odf_xyz[2];
}
}
// correct for b-table orientation
if(check_b_table)
{
set_title("checking b-table");
bool output_dif = image_model->voxel.output_diffusivity;
bool output_tensor = image_model->voxel.output_tensor;
image_model->voxel.output_diffusivity = false;
image_model->voxel.output_tensor = false;
image_model->reconstruct<dti_process>(thread_count);
image_model->voxel.output_diffusivity = output_dif;
image_model->voxel.output_tensor = output_tensor;
std::vector<std::vector<float> > fib_fa(1);
std::vector<std::vector<float> > fib_dir(1);
fib_fa[0].swap(image_model->voxel.fib_fa);
fib_dir[0].swap(image_model->voxel.fib_dir);
const unsigned char order[18][6] = {
{0,1,2,1,0,0},
{0,1,2,0,1,0},
{0,1,2,0,0,1},
{0,2,1,1,0,0},
{0,2,1,0,1,0},
{0,2,1,0,0,1},
{1,0,2,1,0,0},
{1,0,2,0,1,0},
{1,0,2,0,0,1},
{1,2,0,1,0,0},
{1,2,0,0,1,0},
{1,2,0,0,0,1},
{2,1,0,1,0,0},
{2,1,0,0,1,0},
{2,1,0,0,0,1},
{2,0,1,1,0,0},
{2,0,1,0,1,0},
{2,0,1,0,0,1}};
const char txt[18][6] = {"012fx","012fy","012fz",
"021fx","021fy","021fz",
"102fx","102fy","102fz",
"120fx","120fy","120fz",
"210fx","210fy","210fz",
"201fx","201fy","201fz"};
float result[18] = {0};
float cur_score = evaluate_fib(image_model->voxel.dim,fib_fa,fib_dir).first;
for(int i = 0;i < 18;++i)
{
std::vector<std::vector<float> > new_dir(fib_dir);
flip_fib_dir(new_dir[0],order[i]);
result[i] = evaluate_fib(image_model->voxel.dim,fib_fa,new_dir).first;
}
int best = std::max_element(result,result+18)-result;
if(result[best] > cur_score)
{
out << "." << txt[best];
image_model->flip_b_table(order[best]);
}
}
switch (method_id)
{
case 0: //DSI local max
image_model->voxel.recon_report <<
" The diffusion data were reconstructed using diffusion spectrum imaging (Wedeen et al. MRM, 2005) with a Hanning filter of " << (int)param_values[0] << ".";
if (image_model->voxel.odf_deconvolusion || image_model->voxel.odf_decomposition)
{
if (!image_model->reconstruct<dsi_estimate_response_function>(thread_count))
return "reconstruction canceled";
}
out << ".dsi."<< (int)param_values[0] << ".fib.gz";
if (!image_model->reconstruct<dsi_process>(thread_count))
return "reconstruction canceled";
break;
case 1://DTI
image_model->voxel.recon_report << " The diffusion tensor was calculated.";
out << ".dti.fib.gz";
image_model->voxel.max_fiber_number = 1;
if (!image_model->reconstruct<dti_process>(thread_count))
return "reconstruction canceled";
break;
case 2://QBI
image_model->voxel.recon_report << " The diffusion data was reconstructed using q-ball imaging (Tuch, MRM 2004).";
if (image_model->voxel.odf_deconvolusion || image_model->voxel.odf_decomposition)
{
if (!image_model->reconstruct<qbi_estimate_response_function>(thread_count))
return "reconstruction canceled";
}
out << ".qbi."<< param_values[0] << "_" << param_values[1] << ".fib.gz";
if (!image_model->reconstruct<qbi_process>(thread_count))
return "reconstruction canceled";
break;
case 3://QBI
image_model->voxel.recon_report << " The diffusion data was reconstructed using spherical-harmonic-based q-ball imaging (Descoteaux et al., MRM 2007).";
if (image_model->voxel.odf_deconvolusion || image_model->voxel.odf_decomposition)
{
if (!image_model->reconstruct<qbi_sh_estimate_response_function>(thread_count))
return "reconstruction canceled";
}
out << ".qbi.sh"<< (int) param_values[1] << "." << param_values[0] << ".fib.gz";
if (!image_model->reconstruct<qbi_sh_process>(thread_count))
return "reconstruction canceled";
break;
case 4://GQI
if(param_values[0] == 0.0) // spectral analysis
{
image_model->voxel.recon_report <<
" The diffusion data were reconstructed using generalized q-sampling imaging (Yeh et al., IEEE TMI, ;29(9):1626-35, 2010).";
out << (image_model->voxel.r2_weighted ? ".gqi2.spec.fib.gz":".gqi.spec.fib.gz");
if (!image_model->reconstruct<gqi_spectral_process>(thread_count))
return "reconstruction canceled";
break;
}
image_model->voxel.recon_report <<
" The diffusion data were reconstructed using generalized q-sampling imaging (Yeh et al., IEEE TMI, ;29(9):1626-35, 2010) with a diffusion sampling length ratio of " << (float)param_values[0] << ".";
if (image_model->voxel.odf_deconvolusion || image_model->voxel.odf_decomposition)
{
if (!image_model->reconstruct<gqi_estimate_response_function>(thread_count))
return "reconstruction canceled";
}
if(image_model->voxel.r2_weighted)
image_model->voxel.recon_report << " The ODF calculation was weighted by the square of the diffuion displacement.";
if (image_model->voxel.output_rdi)
out << ".rdi";
out << (image_model->voxel.r2_weighted ? ".gqi2.":".gqi.") << param_values[0] << ".fib.gz";
if (!image_model->reconstruct<gqi_process>(thread_count))
return "reconstruction canceled";
break;
case 6:
image_model->voxel.recon_report
<< " The diffusion data were converted to HARDI using generalized q-sampling method with a regularization parameter of " << param_values[2] << ".";
out << ".hardi."<< param_values[0]
<< ".b" << param_values[1]
<< ".reg" << param_values[2] << ".src.gz";
if (!image_model->reconstruct<hardi_convert_process>(thread_count))
return "reconstruction canceled";
break;
case 7:
image_model->voxel.recon_report
<< " The diffusion data were reconstructed in the MNI space using q-space diffeomorphic reconstruction (Yeh et al., Neuroimage, 58(1):91-9, 2011) to obtain the spin distribution function (Yeh et al., IEEE TMI, ;29(9):1626-35, 2010). "
<< " A diffusion sampling length ratio of "
<< (float)param_values[0] << " was used, and the output resolution was " << param_values[1] << " mm.";
// run gqi to get the spin quantity
std::vector<image::pointer_image<float,3> > tmp;
tmp.swap(image_model->voxel.grad_dev);
if (!image_model->reconstruct<gqi_estimate_response_function>(thread_count))
return "reconstruction canceled";
tmp.swap(image_model->voxel.grad_dev);
out << ".reg" << (int)image_model->voxel.reg_method;
out << "i" << (int)image_model->voxel.interpo_method;
out << (image_model->voxel.r2_weighted ? ".qsdr2.":".qsdr.");
out << param_values[0] << "." << param_values[1] << "mm";
if(image_model->voxel.output_jacobian)
out << ".jac";
if(image_model->voxel.output_mapping)
out << ".map";
if (!image_model->reconstruct<gqi_mni_process>(thread_count))
return "reconstruction canceled";
out << ".R" << (int)std::floor(image_model->voxel.R2*100.0) << ".fib.gz";
break;
}
image_model->save_fib(out.str());
output_name = image_model->file_name + out.str();
}
catch (std::exception& e)
{
output_name = e.what();
return output_name.c_str();
}
catch (...)
{
return "unknown exception";
}
return output_name.c_str();
}
const char* reconstruction(ImageModel* image_model,unsigned int method_id,const float* param_values)
{
static std::string output_name;
try
{
{
std::vector<unsigned int> shell;
if(image_model->voxel.bvalues.front() != 0.0)
shell.push_back(0);
for(unsigned int index = 1;index < image_model->voxel.bvalues.size();++index)
if(std::abs(image_model->voxel.bvalues[index]-image_model->voxel.bvalues[index-1]) > 100)
shell.push_back(index);
image_model->voxel.half_sphere =
(method_id == 7 || method_id == 4) &&
(shell.size() > 5) && (shell[1] - shell[0] <= 3);
image_model->voxel.scheme_balance =
(method_id == 7 || method_id == 4) &&
(shell.size() <= 5) && !shell.empty() &&
image_model->voxel.bvalues.size()-shell.back() < 100;
}
image_model->voxel.recon_report.clear();
image_model->voxel.recon_report.str("");
image_model->voxel.param = param_values;
std::ostringstream out;
if(method_id == 1) // DTI
{
image_model->voxel.need_odf = 0;
image_model->voxel.output_jacobian = 0;
image_model->voxel.output_mapping = 0;
image_model->voxel.scheme_balance = 0;
image_model->voxel.half_sphere = 0;
image_model->voxel.odf_deconvolusion = 0;
image_model->voxel.odf_decomposition = 0;
}
if(method_id != 1) // not DTI
{
out << ".odf" << image_model->voxel.ti.fold;// odf_order
out << ".f" << image_model->voxel.max_fiber_number;
if (image_model->voxel.need_odf)
out << "rec";
if (image_model->voxel.scheme_balance)
out << ".bal";
if (image_model->voxel.half_sphere)
out << ".hs";
if (image_model->voxel.odf_deconvolusion)
{
out << ".de" << param_values[2];
if(image_model->voxel.odf_xyz[0] != 0 ||
image_model->voxel.odf_xyz[1] != 0 ||
image_model->voxel.odf_xyz[2] != 0)
out << ".at_" << image_model->voxel.odf_xyz[0]
<< "_" << image_model->voxel.odf_xyz[1]
<< "_" << image_model->voxel.odf_xyz[2];
}
if (image_model->voxel.odf_decomposition)
{
out << ".dec" << param_values[3] << "m" << (int)param_values[4];
if(image_model->voxel.odf_xyz[0] != 0 ||
image_model->voxel.odf_xyz[1] != 0 ||
image_model->voxel.odf_xyz[2] != 0)
out << ".at_" << image_model->voxel.odf_xyz[0]
<< "_" << image_model->voxel.odf_xyz[1]
<< "_" << image_model->voxel.odf_xyz[2];
}
}
// correct for b-table orientation
{
set_title("checking b-table");
image_model->reconstruct<dti_process>();
std::vector<std::vector<float> > fib_fa(1);
std::vector<std::vector<float> > fib_dir(1);
fib_fa[0].swap(image_model->voxel.fib_fa);
fib_dir[0].swap(image_model->voxel.fib_dir);
unsigned int cur_score = evaluate_fib(image_model->voxel.dim,fib_fa,fib_dir).first;
flip_fib_dir(fib_dir[0],true,false,false);
unsigned int flip_x_score = evaluate_fib(image_model->voxel.dim,fib_fa,fib_dir).first;
flip_fib_dir(fib_dir[0],true,true,false);
unsigned int flip_y_score = evaluate_fib(image_model->voxel.dim,fib_fa,fib_dir).first;
flip_fib_dir(fib_dir[0],false,true,true);
unsigned int flip_z_score = evaluate_fib(image_model->voxel.dim,fib_fa,fib_dir).first;
if(flip_x_score > cur_score &&
flip_x_score > flip_y_score && flip_x_score > flip_z_score)
{
std::cout << "b-table flipped x" << std::endl;
for(unsigned int index = 0;index < image_model->voxel.bvectors.size();++index)
image_model->voxel.bvectors[index][0] = -image_model->voxel.bvectors[index][0];
}
if(flip_y_score > cur_score &&
flip_y_score > flip_x_score && flip_y_score > flip_z_score)
{
std::cout << "b-table flipped y" << std::endl;
for(unsigned int index = 0;index < image_model->voxel.bvectors.size();++index)
image_model->voxel.bvectors[index][1] = -image_model->voxel.bvectors[index][1];
}
if(flip_z_score > cur_score &&
flip_z_score > flip_y_score && flip_z_score > flip_x_score)
{
std::cout << "b-table flipped z" << std::endl;
for(unsigned int index = 0;index < image_model->voxel.bvectors.size();++index)
image_model->voxel.bvectors[index][2] = -image_model->voxel.bvectors[index][2];
}
}
switch (method_id)
{
case 0: //DSI local max
image_model->voxel.recon_report <<
" The diffusion data were reconstructed using diffusion spectrum imaging (Wedeen et al. MRM, 2005) with a Hanning filter of " << (int)param_values[0] << ".";
if (image_model->voxel.odf_deconvolusion || image_model->voxel.odf_decomposition)
{
if (!image_model->reconstruct<dsi_estimate_response_function>())
return "reconstruction calceled";
begin_prog("calculating");
}
out << ".dsi."<< (int)param_values[0] << ".fib.gz";
if (!image_model->reconstruct<dsi_process>(out.str()))
return "reconstruction canceled";
break;
case 1://DTI
image_model->voxel.recon_report << " The diffusion tensor was calculated.";
out << ".dti.fib.gz";
image_model->voxel.max_fiber_number = 1;
if (!image_model->reconstruct<dti_process>(out.str()))
return "reconstruction canceled";
break;
case 2://QBI
image_model->voxel.recon_report << " The diffusion data was reconstructed using q-ball imaging (Tuch, MRM 2004).";
if (image_model->voxel.odf_deconvolusion || image_model->voxel.odf_decomposition)
{
if (!image_model->reconstruct<qbi_estimate_response_function>())
return "reconstruction calceled";
begin_prog("calculating");
}
out << ".qbi."<< param_values[0] << "_" << param_values[1] << ".fib.gz";
if (!image_model->reconstruct<qbi_process>(out.str()))
return "reconstruction canceled";
break;
case 3://QBI
image_model->voxel.recon_report << " The diffusion data was reconstructed using spherical-harmonic-based q-ball imaging (Descoteaux et al., MRM 2007).";
if (image_model->voxel.odf_deconvolusion || image_model->voxel.odf_decomposition)
{
if (!image_model->reconstruct<qbi_sh_estimate_response_function>())
return "reconstruction calceled";
begin_prog("calculating");
}
out << ".qbi.sh"<< (int) param_values[1] << "." << param_values[0] << ".fib.gz";
if (!image_model->reconstruct<qbi_sh_process>(out.str()))
return "reconstruction canceled";
break;
case 4://GQI
if(param_values[0] == 0.0) // spectral analysis
{
image_model->voxel.recon_report <<
" The diffusion data were reconstructed using generalized q-sampling imaging (Yeh et al., IEEE TMI, 2010).";
out << (image_model->voxel.r2_weighted ? ".gqi2.spec.fib.gz":".gqi.spec.fib.gz");
if (!image_model->reconstruct<gqi_spectral_process>(out.str()))
return "reconstruction canceled";
break;
}
image_model->voxel.recon_report <<
" The diffusion data were reconstructed using generalized q-sampling imaging (Yeh et al., IEEE TMI, 2010) with a diffusion sampling length ratio of " << (float)param_values[0] << ".";
if (image_model->voxel.odf_deconvolusion || image_model->voxel.odf_decomposition)
{
if (!image_model->reconstruct<gqi_estimate_response_function>())
return "reconstruction calceled";
begin_prog("calculating");
}
if(image_model->voxel.r2_weighted)
image_model->voxel.recon_report << " The ODF calculation was weighted by the square of the diffuion displacement.";
out << (image_model->voxel.r2_weighted ? ".gqi2.":".gqi.") << param_values[0] << ".fib.gz";
if (!image_model->reconstruct<gqi_process>(out.str()))
return "reconstruction canceled";
break;
case 6:
image_model->voxel.recon_report
<< " The diffusion data were converted to HARDI using generalized q-sampling method with a regularization parameter of " << param_values[2] << ".";
out << ".hardi."<< param_values[0]
<< ".b" << param_values[1]
<< ".reg" << param_values[2] << ".src.gz";
if (!image_model->reconstruct<hardi_convert_process>(out.str()))
return "reconstruction canceled";
break;
case 7:
image_model->voxel.recon_report
<< " The diffusion data were reconstructed using q-space diffeomorphic reconstruction (Yeh et al. Neuroimage, 2011) with a diffusion sampling length ratio of "
<< (float)param_values[0] << ". The output resolution was " << param_values[1] << " mm.";
// run gqi to get the spin quantity
if (!image_model->reconstruct<gqi_estimate_response_function>())
return "reconstruction calceled";
out << ".reg" << (int)image_model->voxel.reg_method;
out << (image_model->voxel.r2_weighted ? ".qsdr2.":".qsdr.");
out << param_values[0] << "." << param_values[1] << "mm";
if(image_model->voxel.output_jacobian)
out << ".jac";
if(image_model->voxel.output_mapping)
out << ".map";
out << ".fib.gz";
begin_prog("deforming");
if (!image_model->reconstruct<gqi_mni_process>(out.str()))
return "reconstruction canceled";
break;
}
output_name = image_model->file_name + out.str();
}
catch (std::exception& e)
{
output_name = e.what();
return output_name.c_str();
}
catch (...)
{
return "unknown exception";
}
return output_name.c_str();
}
