void maxAbsolute(const hoNDArray<T>& x, T& r, size_t& ind)
{
size_t N = x.get_number_of_elements();
const T* pX = x.begin();
ind = 0;
if ( N == 0 ) return;
long long n;
typename realType<T>::Type v = abs(pX[0]);
typename realType<T>::Type v2;
ind = 0;
for ( n=1; n<(long long)N; n++ )
{
v2 = std::abs(pX[n]);
if ( v2 > v )
{
v = v2;
ind = n;
}
}
r = pX[ind];
}
void correct_time_stamp_with_fitting(hoNDArray<float>& time_stamp, size_t startE1, size_t endE1)
{
try
{
size_t E1 = time_stamp.get_size(0);
size_t N = time_stamp.get_size(1);
size_t rE1 = endE1 - startE1 + 1;
size_t e1, n;
size_t num_acq_read_outs = 0;
for ( n=0; n<N; n++ )
{
for ( e1=0; e1<E1; e1++ )
{
if ( time_stamp(e1, n) > 0 )
{
num_acq_read_outs++;
}
}
}
GDEBUG_STREAM(" Number of acquired lines : " << num_acq_read_outs);
float a, b; // y = a + b*x
{
std::vector<float> x(num_acq_read_outs), y(num_acq_read_outs);
size_t ind = 0;
for ( n=0; n<N; n++ )
{
for ( e1=startE1; e1<=endE1; e1++ )
{
float acq_time = time_stamp(e1, n);
if ( acq_time > 0 )
{
x[ind] = (float)(e1-startE1 + n*rE1);
y[ind] = acq_time;
ind++;
}
}
}
Gadgetron::simple_line_fit(x, y, a, b);
}
for ( n=0; n<N; n++ )
{
for ( e1=startE1; e1<=endE1; e1++ )
{
float x_v = (float)(e1-startE1 + n*rE1);
time_stamp(e1, n) = a + b*x_v;
}
}
}
catch(...)
{
GADGET_THROW("Exceptions happened in correct_time_stamp_with_fitting(...) ... ");
}
}
void grappa2d_calib_convolution_kernel(const hoNDArray<T>& acsSrc, const hoNDArray<T>& acsDst, size_t accelFactor, double thres, size_t kRO, size_t kNE1, hoNDArray<T>& convKer)
{
size_t startRO = 0;
size_t endRO = acsSrc.get_size(0) - 1;
size_t startE1 = 0;
size_t endE1 = acsSrc.get_size(1) - 1;
grappa2d_calib_convolution_kernel(acsSrc, acsDst, accelFactor, thres, kRO, kNE1, startRO, endRO, startE1, endE1, convKer);
}
int CS_FOCUSS_2D::fRecon(hoNDArray<std::complex<float> > &hacfInput, hoNDArray<std::complex<float> > &hacfRecon){
// input dimensions
vtDim_ = *hacfInput.get_dimensions();
// number of channels
iNChannels_ = (int)vtDim_[2];
// if Matlab is used, initialize singleton variables
/*if (bMatlab_){
for (int iI = 0; iI < 20; iI++){
GlobalVar_FOCUSS::instance()->vbStatPrinc_.push_back(false);
GlobalVar_FOCUSS::instance()->vfPrincipleComponents_.push_back(new hoNDArray<std::complex<float> > ());
}
}*/
// const complex values
const std::complex<float> cfZero(0.0);
const std::complex<float> cfOne(1.0);
// store incoming data in array
hoNDArray<std::complex<float> > hacfKSpace = hacfInput;
// permute kSpace: x-y-c -> y-x-c
std::vector<size_t> vtDimOrder; vtDimOrder.push_back(1); vtDimOrder.push_back(0); vtDimOrder.push_back(2);
hacfKSpace = *permute(&hacfKSpace, &vtDimOrder,false);
// update dim_ vector
vtDim_.clear(); vtDim_ = *hacfKSpace.get_dimensions();
//------------------------------------------------------------------------
//-------------------------- sampling mask -------------------------------
//------------------------------------------------------------------------
hoNDArray<std::complex<float> > hacfFullMask(hacfKSpace.get_dimensions()); hacfFullMask.fill(cfZero);
hoNDArray<bool> habFullMask(hacfKSpace.get_dimensions()); habFullMask.fill(false);
pcfPtr_ = hacfKSpace.get_data_ptr();
pcfPtr2_ = hacfFullMask.get_data_ptr();
pbPtr_ = habFullMask.get_data_ptr();
for (int i = 0; i < hacfKSpace.get_number_of_elements(); i++)
if (pcfPtr_[i] != cfZero){
pcfPtr2_[i] = cfOne;
pbPtr_[i] = true;
}
//-------------------------------------------------------------------------
//---------------- iFFT x direction - x ky kz ^= v (n�) -------------------
//-------------------------------------------------------------------------
if (Transform_fftBA_->get_active()){
if (!bMatlab_ && bDebug_)
#if __GADGETRON_VERSION_HIGHER_3_6__ == 1
GDEBUG("FFT in read direction..\n");
#else
GADGET_DEBUG1("FFT in read direction..\n");
#endif
else if(bMatlab_ && bDebug_){
// mexPrintf("FFT in read direction..\n");mexEvalString("drawnow;");
}
Transform_fftBA_->FTransform(hacfKSpace);
}
void compute_phase_time_stamp(const hoNDArray<float>& time_stamp, const hoNDArray<float>& cpt_time_stamp, size_t startE1, size_t endE1,
hoNDArray<float>& phs_time_stamp, hoNDArray<float>& phs_cpt_time_stamp)
{
try
{
size_t E1 = time_stamp.get_size(0);
size_t N = time_stamp.get_size(1);
size_t rE1 = endE1 - startE1 + 1;
size_t e1, n;
for ( n=0; n<N; n++ )
{
// phase time stamp as the mean of all aquired lines
size_t num = 0;
float tt = 0.0f;
for ( e1=startE1; e1<=endE1; e1++ )
{
if(time_stamp(e1, n)>0)
{
tt += time_stamp(e1, n);
num++;
}
}
phs_time_stamp(n, 0) = tt/((num>0) ? num : 1);
//// phase cpt time as the median of all acquired lines
//std::vector<float> cpt_buf(rE1, 0);
//for ( e1=startE1; e1<=endE1; e1++ )
//{
// if(cpt_time_stamp(e1, n)>=0)
// cpt_buf[e1-startE1] = cpt_time_stamp(e1, n);
//}
//std::sort(cpt_buf.begin(), cpt_buf.end());
//phs_cpt_time_stamp(n, 0) = cpt_buf[E1/2-startE1];
// phase cpt time as the cpt time of center line
phs_cpt_time_stamp(n, 0) = cpt_time_stamp(E1/2, n);
}
}
catch(...)
{
GADGET_THROW("Exceptions happened in compute_phase_time_stamp(...) ... ");
}
}
void grappa2d_calib_convolution_kernel(const hoNDArray<T>& dataSrc, const hoNDArray<T>& dataDst, hoNDArray<unsigned short>& dataMask, size_t accelFactor, double thres, size_t kRO, size_t kNE1, hoNDArray<T>& convKer)
{
try
{
bool fitItself = false;
if (&dataSrc != &dataDst) fitItself = true;
GADGET_CHECK_THROW(dataSrc.dimensions_equal(&dataMask));
GADGET_CHECK_THROW(dataDst.dimensions_equal(&dataMask));
// find the fully sampled region
size_t RO = dataMask.get_size(0);
size_t E1 = dataMask.get_size(1);
size_t srcCHA = dataSrc.get_size(2);
size_t dstCHA = dataDst.get_size(2);
size_t startRO(0), endRO(0), startE1(0), endE1(0);
size_t ro, e1, scha, dcha;
for (e1 = 0; e1 < E1; e1++)
{
for (ro = 0; ro < RO; ro++)
{
if (dataMask(ro, e1)>0)
{
if (ro < startRO) startRO = ro;
if (ro > endRO) endRO = ro;
if (e1 < startE1) startE1 = e1;
if (e1 > endE1) endE1 = e1;
}
}
}
GADGET_CHECK_THROW(endRO>startRO);
GADGET_CHECK_THROW(endE1>startE1 + accelFactor);
GADGET_CATCH_THROW(grappa2d_calib_convolution_kernel(dataSrc, dataDst, accelFactor, thres, kRO, kNE1, startRO, endRO, startE1, endE1, convKer));
}
catch (...)
{
GADGET_THROW("Errors in grappa2d_calib_convolution_kernel(dataMask) ... ");
}
}
void apply_unmix_coeff_kspace(const hoNDArray<T>& kspace, const hoNDArray<T>& unmixCoeff, hoNDArray<T>& complexIm)
{
try
{
GADGET_CHECK_THROW(kspace.get_size(0) == unmixCoeff.get_size(0));
GADGET_CHECK_THROW(kspace.get_size(1) == unmixCoeff.get_size(1));
GADGET_CHECK_THROW(kspace.get_size(2) == unmixCoeff.get_size(2));
hoNDArray<T> buffer2DT(kspace);
GADGET_CATCH_THROW(Gadgetron::hoNDFFT<typename realType<T>::Type>::instance()->ifft2c(kspace, buffer2DT));
std::vector<size_t> dim;
kspace.get_dimensions(dim);
dim[2] = 1;
if (!complexIm.dimensions_equal(&dim))
{
complexIm.create(&dim);
}
Gadgetron::multiply(buffer2DT, unmixCoeff, buffer2DT);
Gadgetron::sum_over_dimension(buffer2DT, complexIm, 2);
}
catch (...)
{
GADGET_THROW("Errors in apply_unmix_coeff_kspace(const hoNDArray<T>& kspace, const hoNDArray<T>& unmixCoeff, hoNDArray<T>& complexIm) ... ");
}
}
void apply_unmix_coeff_aliased_image(const hoNDArray<T>& aliasedIm, const hoNDArray<T>& unmixCoeff, hoNDArray<T>& complexIm)
{
try
{
GADGET_CHECK_THROW(aliasedIm.get_size(0) == unmixCoeff.get_size(0));
GADGET_CHECK_THROW(aliasedIm.get_size(1) == unmixCoeff.get_size(1));
GADGET_CHECK_THROW(aliasedIm.get_size(2) == unmixCoeff.get_size(2));
std::vector<size_t> dim;
aliasedIm.get_dimensions(dim);
dim[2] = 1;
if (!complexIm.dimensions_equal(&dim))
{
complexIm.create(&dim);
}
hoNDArray<T> buffer2DT(aliasedIm);
Gadgetron::multiply(aliasedIm, unmixCoeff, buffer2DT);
Gadgetron::sum_over_dimension(buffer2DT, complexIm, 2);
}
catch (...)
{
GADGET_THROW("Errors in apply_unmix_coeff_aliased_image(const hoNDArray<T>& aliasedIm, const hoNDArray<T>& unmixCoeff, hoNDArray<T>& complexIm) ... ");
}
}
hoNDArray<uint16_t>
update_field_map(const hoNDArray<uint16_t> &field_map_index, const hoNDArray<uint16_t> &proposed_field_map_index,
const hoNDArray<float> &residuals_map, const hoNDArray<float> &lambda_map) {
hoNDArray<float> residual_diff_map(field_map_index.get_dimensions());
const auto X = field_map_index.get_size(0);
const auto Y = field_map_index.get_size(1);
const auto Z = field_map_index.get_size(2);
for (size_t kz = 0; kz < Z; kz++) {
for (size_t ky = 0; ky < Y; ky++) {
for (size_t kx = 0; kx < X; kx++) {
residual_diff_map(kx, ky,kz) = residuals_map(field_map_index(kx, ky,kz), kx, ky,kz) -
residuals_map(proposed_field_map_index(kx, ky,kz), kx, ky,kz);
}
}
}
std::vector<boost::default_color_type> color_map;
if (Z == 1) {
color_map = graph_cut<2>(field_map_index, proposed_field_map_index, lambda_map,
residual_diff_map);
} else {
color_map = graph_cut<3>(field_map_index, proposed_field_map_index, lambda_map, residual_diff_map);
}
auto result = field_map_index;
size_t updated_voxels = 0;
for (size_t i = 0; i < field_map_index.get_number_of_elements(); i++) {
if (color_map[i] != boost::default_color_type::black_color) {
updated_voxels++;
result[i] = proposed_field_map_index[i];
}
}
return result;
}
void maxValue(const hoNDArray<T>& a, T& v)
{
typedef T ValueType;
try
{
const ValueType* pA = a.begin();
size_t n = a.get_number_of_elements();
v = pA[0];
size_t ii;
for (ii=1; ii<n; ii++)
{
if (pA[ii]>v) v = pA[ii];
}
}
catch(...)
{
GADGET_THROW("Errors in maxValue(const hoNDArray<T>& a, T& v) ... ");
}
}
void sort(const hoNDArray<T>& x, hoNDArray<T>& r, bool isascending)
{
if ( &r != &x )
{
if ( r.get_number_of_elements()!=x.get_number_of_elements())
{
r = x;
}
else
{
memcpy(r.begin(), x.begin(), x.get_number_of_bytes());
}
}
sort(x.get_number_of_elements(), x.begin(), r.begin(), isascending);
}
void outputPlotIm(const hoNDArray<unsigned char>& im, bool trueColor, hoNDArray<float>& plotIm)
{
size_t xsize = im.get_size(1);
size_t ysize = im.get_size(2);
plotIm.copyFrom(im);
if (trueColor)
{
std::vector<size_t> dim_order(3);
dim_order[0] = 1;
dim_order[1] = 2;
dim_order[2] = 0;
hoNDArray<float> plotImPermuted;
plotImPermuted.copyFrom(plotIm);
plotIm.create(xsize, ysize, 3);
Gadgetron::permute(plotImPermuted, plotIm, dim_order);
}
else
{
hoNDArray<float> plotIm2D;
Gadgetron::sum_over_dimension(plotIm, plotIm2D, 0);
plotIm2D.squeeze();
std::vector<size_t> dim_order(2);
dim_order[0] = 0;
dim_order[1] = 1;
plotIm.create(xsize, ysize);
Gadgetron::permute(plotIm2D, plotIm, dim_order);
}
}
hoNDArray< std::complex<float> > fatwater_separation(hoNDArray< std::complex<float> >& data, FatWaterParameters p, FatWaterAlgorithm a)
{
//Get some data parameters
//7D, fixed order [X, Y, Z, CHA, N, S, LOC]
uint16_t X = data.get_size(0);
uint16_t Y = data.get_size(1);
uint16_t Z = data.get_size(2);
uint16_t CHA = data.get_size(3);
uint16_t N = data.get_size(4);
uint16_t S = data.get_size(5);
uint16_t LOC = data.get_size(6);
GDEBUG("Size of my array: %d, %d, %d .\n", X,Y,Z);
hoNDArray< std::complex<float> > out(X,Y,Z,CHA,N,2,LOC); // S dimension gets replaced by water/fat stuff
float fieldStrength = p.fieldStrengthT_;
std::vector<float> echoTimes = p.echoTimes_;
bool precessionIsClockwise = p.precessionIsClockwise_;
for (auto& te: echoTimes) {
te = te*0.001; // Echo times in seconds rather than milliseconds
}
GDEBUG("In toolbox - Field Strength: %f T \n", fieldStrength);
for (auto& te: echoTimes) {
GDEBUG("In toolbox - Echo time: %f seconds \n", te);
}
GDEBUG("In toolbox - PrecessionIsClockwise: %d \n", precessionIsClockwise);
//Get or set some algorithm parameters
//Gadgetron::ChemicalSpecies w = a.species_[0];
//Gadgetron::ChemicalSpecies f = a.species_[1];
// GDEBUG("In toolbox - Fat peaks: %f \n", f.ampFreq_[0].first);
// GDEBUG("In toolbox - Fat peaks 2: %f \n", f.ampFreq_[0].second);
// Set some initial parameters so we can get going
// These will have to be specified in the XML file eventually
std::pair<float,float> range_r2star = std::make_pair(0.0,0.0);
uint16_t num_r2star = 1;
std::pair<float,float> range_fm = std::make_pair(-80.0,80.0);
uint16_t num_fm = 101;
uint16_t num_iterations = 40;
uint16_t subsample = 1;
float lmap_power = 2.0;
float lambda = 0.02;
float lambda_extra = 0.02;
//Check that we have reasonable data for fat-water separation
//Calculate residual
//
float relAmp, freq_hz;
uint16_t npeaks;
uint16_t nspecies = a.species_.size();
uint16_t nte = echoTimes.size();
GDEBUG("In toolbox - NTE: %d \n", nte);
hoMatrix< std::complex<float> > phiMatrix(nte,nspecies);
for( int k1=0;k1<nte;k1++) {
for( int k2=0;k2<nspecies;k2++) {
phiMatrix(k1,k2) = 0.0;
npeaks = a.species_[k2].ampFreq_.size();
for( int k3=0;k3<npeaks;k3++) {
relAmp = a.species_[k2].ampFreq_[k3].first;
freq_hz = fieldStrength*GAMMABAR*a.species_[k2].ampFreq_[k3].second;
phiMatrix(k1,k2) += relAmp*std::complex<float>(cos(2*PI*echoTimes[k1]*freq_hz),sin(2*PI*echoTimes[k1]*freq_hz));
}
GDEBUG("Cur value phiMatrix = (%f,%f) \n", phiMatrix(k1,k2).real(), phiMatrix(k1,k2).imag());
}
}
//auto a_phiMatrix = as_arma_matrix(&phiMatrix);
//auto mymat2 = mymat.t()*mymat;
for(int ka=0;ka<phiMatrix.get_size(0);ka++) {
for(int kb=0;kb<phiMatrix.get_size(1);kb++) {
GDEBUG("Check phiMatrix(%d,%d) = %f + i %f \n", ka,kb,phiMatrix(ka,kb).real(),phiMatrix(ka,kb).imag());
}
}
hoMatrix< std::complex<float> > IdentMat(nte,nte);
for( int k1=0;k1<nte;k1++) {
for( int k2=0;k2<nte;k2++) {
if( k1==k2 ) {
IdentMat(k1,k2) = std::complex<float>(1.0,0.0);
} else {
IdentMat(k1,k2) = std::complex<float>(0.0,0.0);
}
}
}
// auto a_phiMatrix = as_arma_matrix(&IdentMat);
float fm;
std::vector<float> fms(num_fm);
fms[0] = range_fm.first;
for(int k1=1;k1<num_fm;k1++) {
fms[k1] = range_fm.first + k1*(range_fm.second-range_fm.first)/(num_fm-1);
}
float r2star;
std::vector<float> r2stars(num_r2star);
r2stars[0] = range_r2star.first;
for(int k2=1;k2<num_r2star;k2++) {
r2stars[k2] = range_r2star.first + k2*(range_r2star.second-range_r2star.first)/(num_r2star-1);
}
std::complex<float> curModulation;
hoMatrix< std::complex<float> > tempM1(nspecies,nspecies);
hoMatrix< std::complex<float> > tempM2(nspecies,nte);
hoMatrix< std::complex<float> > psiMatrix(nte,nspecies);
hoNDArray< std::complex<float> > Ps(nte,nte,num_fm,num_r2star);
hoMatrix< std::complex<float> > P1(nte,nte);
hoMatrix< std::complex<float> > P(nte,nte);
for(int k3=0;k3<num_fm;k3++) {
fm = fms[k3];
for(int k4=0;k4<num_r2star;k4++) {
r2star = r2stars[k4];
for( int k1=0;k1<nte;k1++) {
curModulation = exp(-r2star*echoTimes[k1])*std::complex<float>(cos(2*PI*echoTimes[k1]*fm),sin(2*PI*echoTimes[k1]*fm));
for( int k2=0;k2<nspecies;k2++) {
psiMatrix(k1,k2) = phiMatrix(k1,k2)*curModulation;
}
}
herk( tempM1, psiMatrix, 'L', true );
// tempM1.copyLowerTriToUpper();
for (int ka=0;ka<tempM1.get_size(0);ka++ ) {
for (int kb=ka+1;kb<tempM1.get_size(1);kb++ ) {
tempM1(ka,kb) = conj(tempM1(kb,ka));
}
}
if(k3==50) {
for(int ka=0;ka<tempM1.get_size(0);ka++) {
for(int kb=0;kb<tempM1.get_size(1);kb++) {
GDEBUG(" tempM1(%d,%d) = %f + i %f \n", ka,kb,tempM1(ka,kb).real(),tempM1(ka,kb).imag());
}
}
}
potri(tempM1);
for (int ka=0;ka<tempM1.get_size(0);ka++ ) {
for (int kb=ka+1;kb<tempM1.get_size(1);kb++ ) {
tempM1(ka,kb) = conj(tempM1(kb,ka));
}
}
if(k3==50) {
for(int ka=0;ka<tempM1.get_size(0);ka++) {
for(int kb=0;kb<tempM1.get_size(1);kb++) {
GDEBUG(" inv tempM1(%d,%d) = %f + i %f \n", ka,kb,tempM1(ka,kb).real(),tempM1(ka,kb).imag());
}
}
}
//GDEBUG(" (%d,%d) = (%d,%d) X (%d,%d) \n", tempM2.get_size(0),tempM2.get_size(1),tempM1.get_size(0),tempM1.get_size(1),psiMatrix.get_size(1),psiMatrix.get_size(0));
gemm( tempM2, tempM1, false, psiMatrix, true );
if(k3==50) {
for(int ka=0;ka<tempM2.get_size(0);ka++) {
for(int kb=0;kb<tempM2.get_size(1);kb++) {
GDEBUG(" tempM2(%d,%d) = %f + i %f \n", ka,kb,tempM2(ka,kb).real(),tempM2(ka,kb).imag());
}
}
}
//GDEBUG(" (%d,%d) = (%d,%d) X (%d,%d) \n", P1.get_size(0),P1.get_size(1),psiMatrix.get_size(0),psiMatrix.get_size(1),tempM2.get_size(0),tempM2.get_size(1));
gemm( P1, psiMatrix, false, tempM2, false );
if(k3==50) {
for(int ka=0;ka<P1.get_size(0);ka++) {
for(int kb=0;kb<P1.get_size(1);kb++) {
GDEBUG(" P1(%d,%d) = %f + i %f \n", ka,kb,P1(ka,kb).real(),P1(ka,kb).imag());
}
}
}
subtract(IdentMat,P1,P);
if(k3==50) {
for(int ka=0;ka<P.get_size(0);ka++) {
for(int kb=0;kb<P.get_size(1);kb++) {
GDEBUG(" P(%d,%d) = %f + i %f \n", ka,kb,P(ka,kb).real(),P(ka,kb).imag());
}
}
}
// Keep all projector matrices together
for( int k1=0;k1<nte;k1++) {
for( int k2=0;k2<nte;k2++) {
Ps(k1,k2,k3,k4) = P(k1,k2);
}
}
}
}
// Need to check that S = nte
// N should be the number of contrasts (eg: for PSIR)
hoMatrix< std::complex<float> > tempResVector(S,N);
hoMatrix< std::complex<float> > tempSignal(S,N);
hoNDArray<float> residual(num_fm,X,Y);
hoNDArray<uint16_t> r2starIndex(X,Y,num_fm);
hoNDArray<uint16_t> fmIndex(X,Y);
float curResidual, minResidual, minResidual2;
for( int k1=0;k1<X;k1++) {
for( int k2=0;k2<Y;k2++) {
// Get current signal
for( int k4=0;k4<N;k4++) {
for( int k5=0;k5<S;k5++) {
tempSignal(k5,k4) = data(k1,k2,0,0,k4,k5,0);
if (k1==107 && k2==144) {
tempSignal(k5,k4) = std::complex<float>(1000.0,0.0);;
GDEBUG(" (%d,%d) --> %f + i %f \n",k5,k4, tempSignal(k5,k4).real(),tempSignal(k5,k4).imag());
}
}
}
minResidual2 = 1.0 + nrm2(&tempSignal);
for(int k3=0;k3<num_fm;k3++) {
minResidual = 1.0 + nrm2(&tempSignal);
for(int k4=0;k4<num_r2star;k4++) {
// Get current projector matrix
for( int k5=0;k5<nte;k5++) {
for( int k6=0;k6<nte;k6++) {
P(k5,k6) = Ps(k5,k6,k3,k4);
}
}
// Apply projector
gemm( tempResVector, P, false, tempSignal, false );
curResidual = nrm2(&tempResVector);
if (curResidual < minResidual) {
minResidual = curResidual;
r2starIndex(k1,k2,k3) = k4;
}
}
residual(k3,k1,k2) = minResidual;
if (minResidual < minResidual2) {
minResidual2 = minResidual;
fmIndex(k1,k2) = k3;
}
if (k1==107 && k2==144) {
GDEBUG(" %f --> %f \n",fms[k3],minResidual);
}
}
}
}
//arma::Mat< std::complex<float> > arma_phiMatrix = as_arma_matrix( phiMatrix );
//Do graph cut iterations
using namespace boost;
// create a typedef for the Graph type
typedef adjacency_list<vecS, vecS, bidirectionalS> Graph;
// Make convenient labels for the vertices
enum { A, B, C, D, E };
const int num_vertices = 5;
const char* name = "ABCDE";
// writing out the edges in the graph
typedef std::pair<int, int> Edge;
Edge edge_array[] =
{ Edge(A,B), Edge(A,D), Edge(C,A), Edge(D,C),
Edge(C,E), Edge(B,D), Edge(D,E) };
const int num_edges = sizeof(edge_array)/sizeof(edge_array[0]);
// declare a graph object
Graph g(edge_array, edge_array + sizeof(edge_array) / sizeof(Edge), num_vertices);
/*
property_map<Graph, edge_capacity_t>::type
capacity = get(edge_capacity, g);
property_map<Graph, edge_reverse_t>::type
rev = get(edge_reverse, g);
property_map<Graph, edge_residual_capacity_t>::type
residual_capacity = get(edge_residual_capacity, g);
Traits::vertex_descriptor s, t;
read_dimacs_max_flow(g, capacity, rev, s, t);
long flow;
#if defined(BOOST_MSVC) && BOOST_MSVC <= 1300
// Use non-named parameter version
property_map<Graph, vertex_index_t>::type
indexmap = get(vertex_index, g);
flow = push_relabel_max_flow(g, s, t, capacity, residual_capacity, rev, indexmap);
#else
flow = push_relabel_max_flow(g, s, t);
#endif
std::cout << "c The total flow:" << std::endl;
std::cout << "s " << flow << std::endl << std::endl;
std::cout << "c flow values:" << std::endl;
graph_traits<Graph>::vertex_iterator u_iter, u_end;
graph_traits<Graph>::out_edge_iterator ei, e_end;
for (boost::tie(u_iter, u_end) = vertices(g); u_iter != u_end; ++u_iter)
for (boost::tie(ei, e_end) = out_edges(*u_iter, g); ei != e_end; ++ei)
if (capacity[*ei] > 0)
std::cout << "f " << *u_iter << " " << target(*ei, g) << " "
<< (capacity[*ei] - residual_capacity[*ei]) << std::endl;
*/
//Do final calculations once the field map is done
hoMatrix< std::complex<float> > curWaterFat(2,N);
hoMatrix< std::complex<float> > AhA(2,2);
// Do fat-water separation with current field map and R2* estimates
for( int k1=0;k1<X;k1++) {
for( int k2=0;k2<Y;k2++) {
// Get current signal
for( int k4=0;k4<N;k4++) {
for( int k5=0;k5<S;k5++) {
tempSignal(k5,k4) = data(k1,k2,0,0,k4,k5,0);
}
}
// Get current Psi matrix
fm = fms[fmIndex(k1,k2)];
r2star = r2stars[r2starIndex(k1,k2,fmIndex(k1,k2))];
for( int k3=0;k3<nte;k3++) {
curModulation = exp(-r2star*echoTimes[k3])*std::complex<float>(cos(2*PI*echoTimes[k3]*fm),sin(2*PI*echoTimes[k3]*fm));
for( int k4=0;k4<nspecies;k4++) {
psiMatrix(k3,k4) = phiMatrix(k3,k4)*curModulation;
}
}
// Solve for water and fat
gemm( curWaterFat, psiMatrix, true, tempSignal, false );
herk( AhA, psiMatrix, 'L', true );
// AhA.copyLowerTriToUpper();
for (int ka=0;ka<AhA.get_size(0);ka++ ) {
for (int kb=ka+1;kb<AhA.get_size(1);kb++ ) {
AhA(ka,kb) = conj(AhA(kb,ka));
}
}
hesv(AhA,curWaterFat);
for ( int k4=0;k4<N;k4++ ) {
for ( int k5=0;k5<2;k5++ ) { // 2 elements for water and fat currently
out(k1,k2,0,0,k4,k5,0) = curWaterFat(k5,k4);
}
}
}
}
//Clean up as needed
return out;
}
void correct_heart_beat_time_stamp_with_fitting(hoNDArray<float>& cpt_time_stamp, hoNDArray<int>& ind_hb, size_t startE1, size_t endE1,
const std::vector<size_t>& start_e1_hb, const std::vector<size_t>& end_e1_hb,
const std::vector<size_t>& start_n_hb, const std::vector<size_t>& end_n_hb )
{
try
{
size_t E1 = cpt_time_stamp.get_size(0);
size_t N = cpt_time_stamp.get_size(1);
size_t rE1 = endE1-startE1+1;
size_t e1, n, ind, ii;
size_t num_acq_read_outs = 0;
for ( n=0; n<N; n++ )
{
for ( e1=0; e1<E1; e1++ )
{
if ( cpt_time_stamp(e1, n) >= 0 )
{
num_acq_read_outs++;
}
}
}
size_t numOfHB = start_e1_hb.size();
std::vector<size_t> ind_HB_start(numOfHB);
std::vector<size_t> ind_HB_end(numOfHB);
for ( ind=0; ind<numOfHB; ind++ )
{
ind_HB_start[ind] = start_e1_hb[ind] + start_n_hb[ind] * E1;
ind_HB_end[ind] = end_e1_hb[ind] + end_n_hb[ind] * E1;
}
// --------------------------------------------------------
// fit a line to every heart beat
// --------------------------------------------------------
float a, b;
std::vector<float> A(numOfHB, 0.0f), B(numOfHB, 0.0f);
for ( ind=0; ind<numOfHB; ind++ )
{
std::vector<float> x, y;
size_t cpt;
for ( cpt=ind_HB_start[ind]; cpt<=ind_HB_end[ind]; cpt++ )
{
size_t n = cpt / E1;
size_t e1 = cpt - n*E1;
if(e1>=startE1 && e1<=endE1)
{
if ( cpt_time_stamp[cpt] > -1 )
{
size_t x_ind = (e1-startE1) + n*rE1;
x.push_back( (float)x_ind );
y.push_back(cpt_time_stamp[cpt]);
}
}
}
if ( !x.empty() )
{
Gadgetron::simple_line_fit(x, y, a, b);
A[ind] = a;
B[ind] = b;
}
}
// --------------------------------------------------------
// compute cpt time stamp for every line
// --------------------------------------------------------
size_t num = cpt_time_stamp.get_number_of_elements();
for ( ind=0; ind<num; ind++ )
{
n = ind / E1;
e1 = ind - n*E1;
if(e1>=startE1 && e1<=endE1)
{
// find to which heart beat this line belongs
bool foundHB = false;
for ( ii=0; ii<numOfHB; ii++ )
{
size_t startHB = ind_HB_start[ii];
size_t endHB = ind_HB_end[ii];
if ( ii==0 && ind<=startHB )
{
foundHB = true;
break;
}
if ( ii==numOfHB-1 && ind>=endHB )
{
foundHB = true;
break;
}
if ( ind>=startHB && ind<=endHB )
{
foundHB = true;
break;
}
}
// if cannot find a heart beat, this kspace line will not be used
if ( foundHB && (std::abs(B[ii])>0) )
{
ind_hb(e1, n) = ii;
size_t x_ind = (e1-startE1) + n*rE1;
cpt_time_stamp(e1, n) = (float)(A[ii] + B[ii]*x_ind);
}
else
{
ind_hb(e1, n) = -1;
}
}
else
{
cpt_time_stamp(e1, n) = -1;
ind_hb(e1, n) = -1;
}
}
}
catch(...)
{
GADGET_THROW("Exceptions happened in correct_heart_beat_time_stamp_with_fitting(...) ... ");
}
}
void dotu(const hoNDArray<T>& x, const hoNDArray<T>& y, T& r)
{
GADGET_DEBUG_CHECK_THROW(x.get_number_of_elements()==y.get_number_of_elements());
dotu(x.get_number_of_elements(), x.begin(), y.begin(), r);
}
template<class T> size_t amax(const hoNDArray<T>& x)
{
return amax(x.get_number_of_elements(), x.begin());
}
void grappa2d_convert_to_convolution_kernel(const hoNDArray<T>& ker, size_t kRO, const std::vector<int>& kE1, const std::vector<int>& oE1, hoNDArray<T>& convKer)
{
try
{
long long srcCHA = (long long)(ker.get_size(2));
long long dstCHA = (long long)(ker.get_size(3));
long long kNE1 = (long long)(kE1.size());
long long oNE1 = (long long)(oE1.size());
long long kROhalf = kRO / 2;
if (2 * kROhalf == kRO)
{
GWARN_STREAM("grappa2d_convert_to_convolution_kernel - 2*kROhalf == kRO " << kRO);
}
kRO = 2 * kROhalf + 1;
//// fill the convolution kernels
long long convKRO = 2 * kRO + 3;
long long maxKE1 = std::abs(kE1[0]);
if (std::abs(kE1[kNE1 - 1]) > maxKE1)
{
maxKE1 = std::abs(kE1[kNE1 - 1]);
}
long long convKE1 = 2 * maxKE1 + 1;
//// allocate the convolution kernel
convKer.create(convKRO, convKE1, srcCHA, dstCHA);
Gadgetron::clear(&convKer);
//// index
long long oe1, kro, ke1, src, dst;
//// fill the convolution kernel and sum up multiple kernels
for (oe1 = 0; oe1<oNE1; oe1++)
{
for (ke1 = 0; ke1<kNE1; ke1++)
{
for (kro = -kROhalf; kro <= kROhalf; kro++)
{
for (dst = 0; dst<dstCHA; dst++)
{
for (src = 0; src<srcCHA; src++)
{
convKer(-kro + kRO + 1, oE1[oe1] - kE1[ke1] + maxKE1, src, dst) = ker(kro + kROhalf, ke1, src, dst, oe1);
}
}
}
}
}
if (oE1[0] != 0)
{
for (dst = 0; dst<dstCHA; dst++)
{
convKer(kRO + 1, maxKE1, dst, dst) = 1.0;
}
}
}
catch (...)
{
GADGET_THROW("Errors in grappa2d_convert_to_convolution_kernel(...) ... ");
}
return;
}
void detect_heart_beat_with_time_stamp(hoNDArray<float>& cpt_time_stamp, hoNDArray<int>& ind_hb,
std::vector<size_t>& start_e1_hb, std::vector<size_t>& end_e1_hb,
std::vector<size_t>& start_n_hb, std::vector<size_t>& end_n_hb )
{
try
{
size_t E1 = cpt_time_stamp.get_size(0);
size_t N = cpt_time_stamp.get_size(1);
size_t e1, n, ind, ii;
size_t num_acq_read_outs = 0;
for ( n=0; n<N; n++ )
{
for ( e1=0; e1<E1; e1++ )
{
if ( cpt_time_stamp(e1, n) >= 0 )
{
num_acq_read_outs++;
}
}
}
ind_hb.create(E1, N);
Gadgetron::clear(ind_hb);
// --------------------------------------------------------
// cpt time stamps
// --------------------------------------------------------
std::vector<float> acquired_cpt(num_acq_read_outs);
std::vector<size_t> ind_acquired_cpt(num_acq_read_outs);
ind = 0;
for ( n=0; n<N; n++ )
{
for ( e1=0; e1<E1; e1++ )
{
if ( cpt_time_stamp(e1, n) > -1 )
{
acquired_cpt[ind] = cpt_time_stamp(e1, n);
ind_acquired_cpt[ind] = e1 + n*E1;
ind++;
}
}
}
// --------------------------------------------------------
// find the number of heart beats
// --------------------------------------------------------
size_t numOfHB = 0;
// store the line indexes for every heart beat
std::vector<size_t> ind_HB_start, ind_HB_end;
ind_HB_start.push_back(0);
for ( ind=1; ind<num_acq_read_outs; ind++ )
{
if ( acquired_cpt[ind] < acquired_cpt[ind-1] )
{
// find a new heart beat
numOfHB++;
size_t end_ind_prev_HB = ind_acquired_cpt[ind-1];
size_t start_ind_curr_HB = ind_acquired_cpt[ind];
// if there is a gap between end and start ind, fill the gap
if ( end_ind_prev_HB+1 != start_ind_curr_HB )
{
long long gap = start_ind_curr_HB - end_ind_prev_HB - 1;
if ( gap % 2 == 0 )
{
end_ind_prev_HB += gap;
}
else
{
end_ind_prev_HB += gap;
}
if ( end_ind_prev_HB+1 != start_ind_curr_HB )
{
GWARN_STREAM("end_ind_prev_HB+1 ~= start_ind_curr_HB : " << end_ind_prev_HB << " " << start_ind_curr_HB);
}
}
ind_HB_end.push_back( end_ind_prev_HB );
ind_HB_start.push_back( start_ind_curr_HB );
}
}
ind_HB_end.push_back( E1*N-1 );
numOfHB = ind_HB_end.size();
// --------------------------------------------------------
// fill the start and end indexes
// --------------------------------------------------------
start_e1_hb.resize(numOfHB, 0);
end_e1_hb.resize(numOfHB, 0);
start_n_hb.resize(numOfHB, 0);
end_n_hb.resize(numOfHB, 0);
std::vector<size_t> start, end;
for ( ii=0; ii<numOfHB; ii++ )
{
start_n_hb[ii] = ind_HB_start[ii] / E1;
start_e1_hb[ii] = ind_HB_start[ii] - start_n_hb[ii] * E1;
end_n_hb[ii] = ind_HB_end[ii] / E1;
end_e1_hb[ii] = ind_HB_end[ii] - end_n_hb[ii]*E1;
for (ind=ind_HB_start[ii]; ind<=ind_HB_end[ii]; ind++)
{
ind_hb(ind) = ii;
}
}
}
catch(...)
{
GADGET_THROW("Exceptions happened in detect_heart_beat_with_time_stamp(...) ... ");
}
}
void GenericReconCartesianGrappaGadget::prepare_down_stream_coil_compression_ref_data(
const hoNDArray<std::complex<float> > &ref_src, hoNDArray<std::complex<float> > &ref_coil_map,
hoNDArray<std::complex<float> > &ref_dst, size_t e) {
if (!downstream_coil_compression.value()) {
GDEBUG_CONDITION_STREAM(verbose.value(), "Downstream coil compression is not prescribed ... ");
ref_dst = ref_src;
return;
}
if (downstream_coil_compression_thres.value() < 0 && downstream_coil_compression_num_modesKept.value() == 0) {
GDEBUG_CONDITION_STREAM(verbose.value(),
"Downstream coil compression is prescribed to use all input channels ... ");
ref_dst = ref_src;
return;
}
// determine how many channels to use
size_t RO = ref_src.get_size(0);
size_t E1 = ref_src.get_size(1);
size_t E2 = ref_src.get_size(2);
size_t CHA = ref_src.get_size(3);
size_t N = ref_src.get_size(4);
size_t S = ref_src.get_size(5);
size_t SLC = ref_src.get_size(6);
size_t recon_RO = ref_coil_map.get_size(0);
size_t recon_E1 = ref_coil_map.get_size(1);
size_t recon_E2 = ref_coil_map.get_size(2);
std::complex<float> *pRef = const_cast< std::complex<float> * >(ref_src.begin());
size_t dstCHA = CHA;
if (downstream_coil_compression_num_modesKept.value() > 0 &&
downstream_coil_compression_num_modesKept.value() <= CHA) {
dstCHA = downstream_coil_compression_num_modesKept.value();
} else {
std::vector<float> E(CHA, 0);
long long cha;
#pragma omp parallel default(none) private(cha) shared(RO, E1, E2, CHA, pRef, E)
{
hoNDArray<std::complex<float> > dataCha;
#pragma omp for
for (cha = 0; cha < (long long) CHA; cha++) {
dataCha.create(RO, E1, E2, pRef + cha * RO * E1 * E2);
float v = Gadgetron::nrm2(dataCha);
E[cha] = v * v;
}
}
for (cha = 1; cha < (long long) CHA; cha++) {
if (std::abs(E[cha]) < downstream_coil_compression_thres.value() * std::abs(E[0])) {
break;
}
}
dstCHA = cha;
}
GDEBUG_CONDITION_STREAM(verbose.value(),
"Downstream coil compression is prescribed to use " << dstCHA << " out of " << CHA
<< " channels ...");
if (dstCHA < CHA) {
ref_dst.create(RO, E1, E2, dstCHA, N, S, SLC);
hoNDArray<std::complex<float> > ref_coil_map_dst;
ref_coil_map_dst.create(recon_RO, recon_E1, recon_E2, dstCHA, N, S, SLC);
size_t slc, s, n;
for (slc = 0; slc < SLC; slc++) {
for (s = 0; s < S; s++) {
for (n = 0; n < N; n++) {
std::complex<float> *pDst = &(ref_dst(0, 0, 0, 0, n, s, slc));
const std::complex<float> *pSrc = &(ref_src(0, 0, 0, 0, n, s, slc));
memcpy(pDst, pSrc, sizeof(std::complex<float>) * RO * E1 * E2 * dstCHA);
pDst = &(ref_coil_map_dst(0, 0, 0, 0, n, s, slc));
pSrc = &(ref_coil_map(0, 0, 0, 0, n, s, slc));
memcpy(pDst, pSrc, sizeof(std::complex<float>) * recon_RO * recon_E1 * recon_E2 * dstCHA);
}
}
}
ref_coil_map = ref_coil_map_dst;
} else {
ref_dst = ref_src;
}
}
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);
// 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());
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");
// perform nonlinear reconstruction
{
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;
}
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());
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();
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;
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());
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();
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;
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 grappa2d_unmixing_coeff(const hoNDArray<T>& kerIm, const hoNDArray<T>& coilMap, size_t acceFactorE1, hoNDArray<T>& unmixCoeff, hoNDArray< typename realType<T>::Type >& gFactor)
{
try
{
typedef typename realType<T>::Type value_type;
size_t RO = kerIm.get_size(0);
size_t E1 = kerIm.get_size(1);
size_t srcCHA = kerIm.get_size(2);
size_t dstCHA = kerIm.get_size(3);
GADGET_CHECK_THROW(acceFactorE1 >= 1);
GADGET_CHECK_THROW(coilMap.get_size(0) == RO);
GADGET_CHECK_THROW(coilMap.get_size(1) == E1);
GADGET_CHECK_THROW(coilMap.get_size(2) == dstCHA);
std::vector<size_t> dimUnmixing(3);
dimUnmixing[0] = RO; dimUnmixing[1] = E1; dimUnmixing[2] = srcCHA;
if (!unmixCoeff.dimensions_equal(&dimUnmixing))
{
unmixCoeff.create(RO, E1, srcCHA);
}
Gadgetron::clear(&unmixCoeff);
std::vector<size_t> dimGFactor(2);
dimGFactor[0] = RO; dimGFactor[1] = E1;
if (!gFactor.dimensions_equal(&dimGFactor))
{
gFactor.create(RO, E1);
}
Gadgetron::clear(&gFactor);
int src;
T* pKerIm = const_cast<T*>(kerIm.begin());
T* pCoilMap = const_cast<T*>(coilMap.begin());
T* pCoeff = unmixCoeff.begin();
std::vector<size_t> dim(2);
dim[0] = RO;
dim[1] = E1;
#pragma omp parallel default(none) private(src) shared(RO, E1, srcCHA, dstCHA, pKerIm, pCoilMap, pCoeff, dim)
{
hoNDArray<T> coeff2D, coeffTmp(&dim);
hoNDArray<T> coilMap2D;
hoNDArray<T> kerIm2D;
#pragma omp for
for (src = 0; src<(int)srcCHA; src++)
{
coeff2D.create(&dim, pCoeff + src*RO*E1);
for (size_t dst = 0; dst<dstCHA; dst++)
{
kerIm2D.create(&dim, pKerIm + src*RO*E1 + dst*RO*E1*srcCHA);
coilMap2D.create(&dim, pCoilMap + dst*RO*E1);
Gadgetron::multiplyConj(kerIm2D, coilMap2D, coeffTmp);
Gadgetron::add(coeff2D, coeffTmp, coeff2D);
}
}
}
hoNDArray<T> conjUnmixCoeff(unmixCoeff);
Gadgetron::multiplyConj(unmixCoeff, conjUnmixCoeff, conjUnmixCoeff);
// Gadgetron::sumOverLastDimension(conjUnmixCoeff, gFactor);
hoNDArray<T> gFactorBuf(RO, E1, 1);
Gadgetron::sum_over_dimension(conjUnmixCoeff, gFactorBuf, 2);
Gadgetron::sqrt(gFactorBuf, gFactorBuf);
Gadgetron::scal((value_type)(1.0 / acceFactorE1), gFactorBuf);
Gadgetron::complex_to_real(gFactorBuf, gFactor);
}
catch (...)
{
GADGET_THROW("Errors in grappa2d_unmixing_coeff(const hoNDArray<T>& kerIm, const hoNDArray<T>& coilMap, hoNDArray<T>& unmixCoeff, hoNDArray<T>& gFactor) ... ");
}
}
void grappa2d_calib(const hoNDArray<T>& acsSrc, const hoNDArray<T>& acsDst, double thres, size_t kRO, const std::vector<int>& kE1, const std::vector<int>& oE1, size_t startRO, size_t endRO, size_t startE1, size_t endE1, hoNDArray<T>& ker)
{
try
{
GADGET_CHECK_THROW(acsSrc.get_size(0)==acsDst.get_size(0));
GADGET_CHECK_THROW(acsSrc.get_size(1)==acsDst.get_size(1));
GADGET_CHECK_THROW(acsSrc.get_size(2)>=acsDst.get_size(2));
size_t RO = acsSrc.get_size(0);
size_t E1 = acsSrc.get_size(1);
size_t srcCHA = acsSrc.get_size(2);
size_t dstCHA = acsDst.get_size(2);
const T* pSrc = acsSrc.begin();
const T* pDst = acsDst.begin();
long long kROhalf = kRO/2;
if ( 2*kROhalf == kRO )
{
GWARN_STREAM("grappa<T>::calib(...) - 2*kROhalf == kRO " << kRO);
}
kRO = 2*kROhalf + 1;
size_t kNE1 = kE1.size();
size_t oNE1 = oE1.size();
/// allocate kernel
ker.create(kRO, kNE1, srcCHA, dstCHA, oNE1);
/// loop over the calibration region and assemble the equation
/// Ax = b
size_t sRO = startRO + kROhalf;
size_t eRO = endRO - kROhalf;
size_t sE1 = std::abs(kE1[0]) + startE1;
size_t eE1 = endE1 - kE1[kNE1-1];
size_t lenRO = eRO - sRO + 1;
size_t rowA = (eE1-sE1+1)*lenRO;
size_t colA = kRO*kNE1*srcCHA;
size_t colB = dstCHA*oNE1;
hoMatrix<T> A;
hoMatrix<T> B;
hoMatrix<T> x( colA, colB );
hoNDArray<T> A_mem(rowA, colA);
A.createMatrix( rowA, colA, A_mem.begin() );
T* pA = A.begin();
hoNDArray<T> B_mem(rowA, colB);
B.createMatrix( A.rows(), colB, B_mem.begin() );
T* pB = B.begin();
long long e1;
for ( e1=(long long)sE1; e1<=(long long)eE1; e1++ )
{
for ( long long ro=sRO; ro<=(long long)eRO; ro++ )
{
long long rInd = (e1-sE1)*lenRO+ro-kROhalf;
size_t src, dst, ke1, oe1;
long long kro;
/// fill matrix A
size_t col = 0;
size_t offset = 0;
for ( src=0; src<srcCHA; src++ )
{
for ( ke1=0; ke1<kNE1; ke1++ )
{
offset = src*RO*E1 + (e1+kE1[ke1])*RO;
for ( kro=-kROhalf; kro<=kROhalf; kro++ )
{
/// A(rInd, col++) = acsSrc(ro+kro, e1+kE1[ke1], src);
pA[rInd + col*rowA] = pSrc[ro+kro+offset];
col++;
}
}
}
/// fill matrix B
col = 0;
for ( oe1=0; oe1<oNE1; oe1++ )
{
for ( dst=0; dst<dstCHA; dst++ )
{
B(rInd, col++) = acsDst(ro, e1+oE1[oe1], dst);
}
}
}
}
SolveLinearSystem_Tikhonov(A, B, x, thres);
memcpy(ker.begin(), x.begin(), ker.get_number_of_bytes());
}
catch(...)
{
GADGET_THROW("Errors in grappa2d_calib(...) ... ");
}
return;
}
template<class T> void asum(const hoNDArray<T>& x, typename realType<T>::Type& r)
{
asum(x.get_number_of_elements(), x.begin(), r);
}
bool plotNoiseStandardDeviation(const hoNDArray< std::complex<T> >& m, const std::vector<std::string>& coilStrings,
const std::string& xlabel, const std::string& ylabel, const std::string& title,
size_t xsize, size_t ysize, bool trueColor,
hoNDArray<float>& plotIm)
{
try
{
size_t CHA = m.get_size(0);
GADGET_CHECK_RETURN_FALSE(coilStrings.size() == CHA);
hoNDArray<double> xd, yd, yd2;
xd.create(CHA);
yd.create(CHA);
size_t c;
for (c = 0; c < CHA; c++)
{
xd(c) = c+1;
yd(c) = std::sqrt( std::abs(m(c, c)) );
}
double maxY = Gadgetron::max(&yd);
yd2 = yd;
std::sort(yd2.begin(), yd2.end());
double medY = yd2(CHA / 2);
// increase dot line to be 1 sigma ~= 33%
double medRange = 0.33;
if (maxY < medY*(1 + medRange))
{
maxY = medY*(1 + medRange);
}
hoNDArray<unsigned char> im;
im.create(3, xsize, ysize);
Gadgetron::clear(im);
plsdev("mem");
plsmem(im.get_size(1), im.get_size(2), im.begin());
plinit();
plfont(2);
pladv(0);
plvpor(0.15, 0.75, 0.1, 0.8);
plwind(0, CHA+1, 0, maxY*1.05);
plcol0(15);
plbox("bcnst", 0.0, 0, "bcnstv", 0.0, 0);
std::string gly;
getPlotGlyph(0, gly); // circle
plstring(CHA, xd.begin(), yd.begin(), gly.c_str());
// draw the median line
pllsty(1);
double px[2], py[2];
px[0] = 0;
px[1] = CHA+1;
py[0] = medY;
py[1] = medY;
plline(2, px, py);
pllsty(2);
py[0] = medY*(1 - medRange);
py[1] = medY*(1 - medRange);
plline(2, px, py);
py[0] = medY*(1 + medRange);
py[1] = medY*(1 + medRange);
plline(2, px, py);
plmtex("b", 3.2, 0.5, 0.5, xlabel.c_str());
plmtex("t", 2.0, 0.5, 0.5, title.c_str());
plmtex("l", 5.0, 0.5, 0.5, ylabel.c_str());
// draw the legend
std::vector<PLINT> opt_array(CHA), text_colors(CHA), line_colors(CHA), line_styles(CHA), symbol_numbers(CHA), symbol_colors(CHA);
std::vector<PLFLT> symbol_scales(CHA), line_widths(CHA), box_scales(CHA, 1);
std::vector<const char*> symbols(CHA);
PLFLT legend_width, legend_height;
std::vector<const char*> legend_text(CHA);
std::vector<std::string> legends(CHA);
size_t n;
for (n = 0; n < CHA; n++)
{
opt_array[n] = PL_LEGEND_SYMBOL;
text_colors[n] = 15;
line_colors[n] = 15;
line_styles[n] = (n % 8 + 1);
line_widths[n] = 0.2;
symbol_colors[n] = 15;
symbol_scales[n] = 0.75;
symbol_numbers[n] = 1;
symbols[n] = gly.c_str();
std::ostringstream ostr;
ostr << n+1 << ":" << coilStrings[n];
legends[n] = ostr.str();
legend_text[n] = legends[n].c_str();
}
pllegend(&legend_width,
&legend_height,
PL_LEGEND_BACKGROUND,
PL_POSITION_OUTSIDE | PL_POSITION_RIGHT,
0.02, // x
0.0, // y
0.05, // plot_width
0, // bg_color
15, // bb_color
1, // bb_style
0, // nrow
0, // ncolumn
CHA, // nlegend
&opt_array[0],
0.05, // text_offset
0.5, // text_scale
1.0, // text_spacing
0.5, // text_justification
&text_colors[0],
(const char **)(&legend_text[0]),
NULL, // box_colors
NULL, // box_patterns
&box_scales[0], // box_scales
NULL, // box_line_widths
&line_colors[0],
&line_styles[0],
&line_widths[0],
&symbol_colors[0],
&symbol_scales[0],
&symbol_numbers[0],
(const char **)(&symbols[0])
);
plend();
outputPlotIm(im, trueColor, plotIm);
}
catch (...)
{
GERROR_STREAM("Errors happened in plotNoiseStandardDeviation(...) ... ");
return false;
}
return true;
}
void norm2(const hoNDArray<T>& x, typename realType<T>::Type& r)
{
norm2(x.get_number_of_elements(), x.begin(), r);
}
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(...) ... ");
}
}
// C = A*B
bool GeneralMatrixProduct(hoNDArray<float>& C, const hoNDArray<float>& A, bool transA, const hoNDArray<float>& B, bool transB)
{
try
{
typedef float T;
size_t M = A.get_size(0);
size_t K = A.get_size(1);
if ( transA )
{
M = A.get_size(1);
K = A.get_size(0);
}
size_t K2 = B.get_size(0);
size_t N = B.get_size(1);
if ( transB )
{
K2 = B.get_size(1);
N = B.get_size(0);
}
GADGET_CHECK_RETURN_FALSE(K==K2);
if ( (C.get_size(0)!=M) || (C.get_size(1)!=N) )
{
C.create(M, N);
}
const T* pA = A.begin();
const T* pB = B.begin();
T* pC = C.begin();
size_t m, n, k;
if ( !transA && !transB )
{
for ( m=0; m<M; m++ )
{
for ( n=0; n<N; n++ )
{
pC[m+n*M] = 0;
for ( k=0; k<K; k++ )
{
pC[m+n*M] += pA[m+k*M]*pB[k+n*K];
}
}
}
}
if ( transA && !transB )
{
for ( m=0; m<M; m++ )
{
for ( n=0; n<N; n++ )
{
pC[m+n*M] = 0;
for ( k=0; k<K; k++ )
{
pC[m+n*M] += pA[k+m*K]*pB[k+n*K];
}
}
}
}
if ( !transA && transB )
{
for ( m=0; m<M; m++ )
{
for ( n=0; n<N; n++ )
{
pC[m+n*M] = 0;
for ( k=0; k<K; k++ )
{
pC[m+n*M] += pA[m+k*M]*pB[n+k*K];
}
}
}
}
if ( transA && transB )
{
for ( m=0; m<M; m++ )
{
for ( n=0; n<N; n++ )
{
pC[m+n*M] = 0;
for ( k=0; k<K; k++ )
{
pC[m+n*M] += pA[k+m*K]*pB[n+k*K];
}
}
}
}
}
catch(...)
{
GERROR_STREAM("Errors in GeneralMatrixProduct(hoNDArray<float>& C, const hoNDArray<float>& A, bool transA, const hoNDArray<float>& B, bool transB) ...");
return false;
}
return true;
}
