struct prox_4pt_dfwavelet_data* prepare_prox_4pt_dfwavelet_data(const long im_dims[DIMS], const long min_size[3], const complex float res[3], unsigned int flow_dim, float lambda, bool use_gpu)
{
PTR_ALLOC(struct prox_4pt_dfwavelet_data, data);
md_copy_dims(DIMS, data->im_dims, im_dims);
md_select_dims(DIMS, FFT_FLAGS, data->tim_dims, im_dims);
md_calc_strides(DIMS, data->im_strs, im_dims, CFL_SIZE);
assert(4 == im_dims[flow_dim]);
// initialize temp
#ifdef USE_CUDA
if (use_gpu) {
data->vx = md_alloc_gpu(DIMS, data->tim_dims, CFL_SIZE);
data->vy = md_alloc_gpu(DIMS, data->tim_dims, CFL_SIZE);
data->vz = md_alloc_gpu(DIMS, data->tim_dims, CFL_SIZE);
data->ph0 = md_alloc_gpu(DIMS, data->tim_dims, CFL_SIZE);
data->pc0 = md_alloc_gpu(DIMS, data->tim_dims, CFL_SIZE);
data->pc1 = md_alloc_gpu(DIMS, data->tim_dims, CFL_SIZE);
data->pc2 = md_alloc_gpu(DIMS, data->tim_dims, CFL_SIZE);
data->pc3 = md_alloc_gpu(DIMS, data->tim_dims, CFL_SIZE);
} else
#endif
{
data->vx = md_alloc(DIMS, data->tim_dims, CFL_SIZE);
data->vy = md_alloc(DIMS, data->tim_dims, CFL_SIZE);
data->vz = md_alloc(DIMS, data->tim_dims, CFL_SIZE);
data->ph0 = md_alloc(DIMS, data->tim_dims, CFL_SIZE);
data->pc0 = md_alloc(DIMS, data->tim_dims, CFL_SIZE);
data->pc1 = md_alloc(DIMS, data->tim_dims, CFL_SIZE);
data->pc2 = md_alloc(DIMS, data->tim_dims, CFL_SIZE);
data->pc3 = md_alloc(DIMS, data->tim_dims, CFL_SIZE);
}
data->flow_dim = flow_dim;
data->slice_flag = ~FFT_FLAGS;
data->lambda = lambda;
data->plan = prepare_dfwavelet_plan( 3, data->tim_dims, (long*) min_size, (complex float*) res, use_gpu );
data->w_op = wavelet_create(DIMS, data->tim_dims, FFT_FLAGS, min_size, true, use_gpu);
data->wthresh_op = prox_unithresh_create(DIMS, data->w_op, lambda, MD_BIT(data->flow_dim), use_gpu);
return data;
}
int main_bpsense(int argc, char* argv[])
{
// -----------------------------------------------------------
// set up conf and option parser
struct bpsense_conf conf = bpsense_defaults;
struct iter_admm_conf iconf = iter_admm_defaults;
conf.iconf = &iconf;
conf.iconf->rho = 10; // more sensibile default
bool usegpu = false;
const char* psf = NULL;
const char* image_truth_fname = NULL;
bool im_truth = false;
bool use_tvnorm = false;
double start_time = timestamp();
const struct opt_s opts[] = {
OPT_FLOAT('e', &conf.eps, "eps", "data consistency error"),
OPT_FLOAT('r', &conf.lambda, "lambda", "l2 regularization parameter"),
OPT_FLOAT('u', &conf.iconf->rho, "rho", "ADMM penalty parameter"),
OPT_SET('c', &conf.rvc, "real-value constraint"),
OPT_SET('t', &use_tvnorm, "use TV norm"),
OPT_STRING('T', &image_truth_fname, "file", "compare to truth image"),
OPT_UINT('i', &conf.iconf->maxiter, "iter", "max. iterations"),
OPT_SET('g', &usegpu, "(use gpu)"),
OPT_STRING('p', &psf, "file", "point-spread function"),
};
cmdline(&argc, argv, 3, 3, usage_str, help_str, ARRAY_SIZE(opts), opts);
if (NULL != image_truth_fname)
im_truth = true;
// -----------------------------------------------------------
// load data and print some info about the recon
int N = DIMS;
long dims[N];
long dims1[N];
long img_dims[N];
long ksp_dims[N];
complex float* kspace_data = load_cfl(argv[1], N, ksp_dims);
complex float* sens_maps = load_cfl(argv[2], N, dims);
for (int i = 0; i < 4; i++) // sizes2[4] may be > 1
if (ksp_dims[i] != dims[i])
error("Dimensions of kspace and sensitivities do not match!\n");
assert(1 == ksp_dims[MAPS_DIM]);
(usegpu ? num_init_gpu : num_init)();
if (dims[MAPS_DIM] > 1)
debug_printf(DP_INFO, "%ld maps.\nESPIRiT reconstruction.\n", dims[4]);
if (conf.lambda > 0.)
debug_printf(DP_INFO, "l2 regularization: %f\n", conf.lambda);
if (use_tvnorm)
debug_printf(DP_INFO, "use Total Variation\n");
else
debug_printf(DP_INFO, "use Wavelets\n");
if (im_truth)
debug_printf(DP_INFO, "Compare to truth\n");
md_select_dims(N, ~(COIL_FLAG | MAPS_FLAG), dims1, dims);
md_select_dims(N, ~COIL_FLAG, img_dims, dims);
// -----------------------------------------------------------
// initialize sampling pattern
complex float* pattern = NULL;
long pat_dims[N];
if (NULL != psf) {
pattern = load_cfl(psf, N, pat_dims);
// FIXME: check compatibility
} else {
pattern = md_alloc(N, dims1, CFL_SIZE);
estimate_pattern(N, ksp_dims, COIL_DIM, pattern, kspace_data);
}
// -----------------------------------------------------------
// print some statistics
size_t T = md_calc_size(N, dims1);
long samples = (long)pow(md_znorm(N, dims1, pattern), 2.);
debug_printf(DP_INFO, "Size: %ld Samples: %ld Acc: %.2f\n", T, samples, (float)T/(float)samples);
// -----------------------------------------------------------
// fftmod to un-center data
fftmod(N, ksp_dims, FFT_FLAGS, kspace_data, kspace_data);
fftmod(N, dims, FFT_FLAGS, sens_maps, sens_maps);
// -----------------------------------------------------------
// apply scaling
float scaling = estimate_scaling(ksp_dims, NULL, kspace_data);
debug_printf(DP_INFO, "Scaling: %f\n", scaling);
if (scaling != 0.)
md_zsmul(N, ksp_dims, kspace_data, kspace_data, 1. / scaling);
// -----------------------------------------------------------
// create l1 prox operator and transform
long minsize[DIMS] = { [0 ... DIMS - 1] = 1 };
minsize[0] = MIN(img_dims[0], 16);
minsize[1] = MIN(img_dims[1], 16);
minsize[2] = MIN(img_dims[2], 16);
const struct linop_s* l1op = NULL;
const struct operator_p_s* l1prox = NULL;
if (use_tvnorm) {
l1op = grad_init(DIMS, img_dims, FFT_FLAGS);
l1prox = prox_thresh_create(DIMS + 1, linop_codomain(l1op)->dims, 1., 0u, usegpu);
conf.l1op_obj = l1op;
} else {
bool randshift = true;
l1op = linop_identity_create(DIMS, img_dims);
conf.l1op_obj = wavelet_create(DIMS, img_dims, FFT_FLAGS, minsize, false, usegpu);
l1prox = prox_wavethresh_create(DIMS, img_dims, FFT_FLAGS, minsize, 1., randshift, usegpu);
}
// -----------------------------------------------------------
// create image and load truth image
complex float* image = create_cfl(argv[3], N, img_dims);
md_clear(N, img_dims, image, CFL_SIZE);
long img_truth_dims[DIMS];
complex float* image_truth = NULL;
if (im_truth)
image_truth = load_cfl(image_truth_fname, DIMS, img_truth_dims);
// -----------------------------------------------------------
// call recon
if (usegpu)
#ifdef USE_CUDA
bpsense_recon_gpu(&conf, dims, image, sens_maps, dims1, pattern, l1op, l1prox, ksp_dims, kspace_data, image_truth);
#else
assert(0);
#endif
else
int main_pocsense(int argc, char* argv[])
{
float alpha = 0.;
int maxiter = 50;
bool l1wav = false;
float lambda = -1.;
bool use_gpu = false;
bool use_admm = false;
float admm_rho = -1.;
int l1type = 2;
const struct opt_s opts[] = {
{ 'i', true, opt_int, &maxiter, NULL },
{ 'r', true, opt_float, &alpha, " alpha\tregularization parameter" },
{ 'l', true, opt_int, &l1type, "1/-l2\t\ttoggle l1-wavelet or l2 regularization" },
{ 'g', false, opt_set, &use_gpu, NULL },
{ 'o', true, opt_float, &lambda, NULL },
{ 'm', true, opt_float, &admm_rho, NULL },
};
cmdline(&argc, argv, 3, 3, usage_str, help_str, ARRAY_SIZE(opts), opts);
if (1 == l1type)
l1wav = true;
else
if (2 == l1type)
l1wav = false;
else
error("Unknown regularization type.");
unsigned int N = DIMS;
long dims[N];
long ksp_dims[N];
complex float* kspace_data = load_cfl(argv[1], N, ksp_dims);
complex float* sens_maps = load_cfl(argv[2], N, dims);
for (int i = 0; i < 4; i++) // sizes2[4] may be > 1
if (ksp_dims[i] != dims[i])
error("Dimensions of kspace and sensitivities do not match!\n");
assert(1 == ksp_dims[MAPS_DIM]);
num_init();
long dims1[N];
md_select_dims(N, ~(COIL_FLAG|MAPS_FLAG), dims1, dims);
// -----------------------------------------------------------
// memory allocation
complex float* result = create_cfl(argv[3], N, ksp_dims);
complex float* pattern = md_alloc(N, dims1, CFL_SIZE);
// -----------------------------------------------------------
// pre-process data
float scaling = estimate_scaling(ksp_dims, NULL, kspace_data);
md_zsmul(N, ksp_dims, kspace_data, kspace_data, 1. / scaling);
estimate_pattern(N, ksp_dims, COIL_DIM, pattern, kspace_data);
// -----------------------------------------------------------
// l1-norm threshold operator
const struct operator_p_s* thresh_op = NULL;
const struct linop_s* wave_op = NULL;
if (l1wav) {
long minsize[DIMS] = { [0 ... DIMS - 1] = 1 };
minsize[0] = MIN(ksp_dims[0], 16);
minsize[1] = MIN(ksp_dims[1], 16);
minsize[2] = MIN(ksp_dims[2], 16);
wave_op = wavelet_create(DIMS, ksp_dims, FFT_FLAGS, minsize, true, use_gpu);
thresh_op = prox_unithresh_create(DIMS, wave_op, alpha, COIL_FLAG, use_gpu);
}
#if 0
else {
int main_pocsense(int argc, char* argv[])
{
int c;
float alpha = 0.;
int maxiter = 50;
bool l1wav = false;
float lambda = -1.;
bool use_gpu = false;
bool use_admm = false;
float admm_rho = 0.1;
while (-1 != (c = getopt(argc, argv, "m:ghi:r:o:l:"))) {
switch (c) {
case 'i':
maxiter = atoi(optarg);
break;
case 'r':
alpha = atof(optarg);
break;
case 'l':
if (1 == atoi(optarg))
l1wav = true;
else
if (2 == atoi(optarg))
l1wav = false;
else {
usage(argv[0], stderr);
exit(1);
}
break;
case 'g':
use_gpu = true;
break;
case 'o':
lambda = atof(optarg);
break;
case 'm':
use_admm = true;
admm_rho = atof(optarg);
break;
case 'h':
usage(argv[0], stdout);
help();
exit(0);
default:
usage(argv[0], stderr);
exit(1);
}
}
if (argc - optind != 3) {
usage(argv[0], stderr);
exit(1);
}
unsigned int N = DIMS;
long dims[N];
long ksp_dims[N];
complex float* kspace_data = load_cfl(argv[optind + 0], N, ksp_dims);
complex float* sens_maps = load_cfl(argv[optind + 1], N, dims);
for (int i = 0; i < 4; i++) { // sizes2[4] may be > 1
if (ksp_dims[i] != dims[i]) {
fprintf(stderr, "Dimensions of kspace and sensitivities do not match!\n");
exit(1);
}
}
assert(1 == ksp_dims[MAPS_DIM]);
num_init();
long dims1[N];
md_select_dims(N, ~(COIL_FLAG|MAPS_FLAG), dims1, dims);
// -----------------------------------------------------------
// memory allocation
complex float* result = create_cfl(argv[optind + 2], N, ksp_dims);
complex float* pattern = md_alloc(N, dims1, CFL_SIZE);
// -----------------------------------------------------------
// pre-process data
float scaling = estimate_scaling(ksp_dims, NULL, kspace_data);
md_zsmul(N, ksp_dims, kspace_data, kspace_data, 1. / scaling);
estimate_pattern(N, ksp_dims, COIL_DIM, pattern, kspace_data);
// -----------------------------------------------------------
// l1-norm threshold operator
const struct operator_p_s* thresh_op = NULL;
const struct linop_s* wave_op = NULL;
if (l1wav) {
long minsize[DIMS] = { [0 ... DIMS - 1] = 1 };
minsize[0] = MIN(ksp_dims[0], 16);
minsize[1] = MIN(ksp_dims[1], 16);
minsize[2] = MIN(ksp_dims[2], 16);
wave_op = wavelet_create(DIMS, ksp_dims, FFT_FLAGS, minsize, true, use_gpu);
thresh_op = prox_unithresh_create(DIMS, wave_op, alpha, COIL_FLAG, use_gpu);
}
#if 0
else {
