def test_espirit_maps_eig(self):
mps_shape = [8, 32, 32]
mps = sim.birdcage_maps(mps_shape)
ksp = sp.fft(mps, axes=[-1, -2])
mps_rec, eig_val = app.EspiritCalib(ksp,
output_eigenvalue=True,
show_pbar=False).run()
np.testing.assert_allclose(eig_val, 1, rtol=0.01, atol=0.01)
def test_espirit_maps(self):
mps_shape = [8, 32, 32]
mps = sim.birdcage_maps(mps_shape)
ksp = sp.fft(mps, axes=[-1, -2])
mps_rec = app.EspiritCalib(ksp, show_pbar=False).run()
np.testing.assert_allclose(np.abs(mps)[:, 8:24, 8:24],
np.abs(mps_rec[:, 8:24, 8:24]),
rtol=1e-2,
atol=1e-2)
def process_slice(kspace, args, calib_method='jsense'):
# get data dimensions
nky, nkz, nechoes, ncoils = kspace.shape
# ESPIRiT parameters
nmaps = args.num_emaps
calib_size = args.ncalib
crop_value = args.crop_value
if args.device is -1:
device = sp.cpu_device
else:
device = sp.Device(args.device)
# compute sensitivity maps (BART)
#cmd = f'ecalib -d 0 -S -m {nmaps} -c {crop_value} -r {calib_size}'
#maps = bart.bart(1, cmd, kspace[:,:,0,None,:])
#maps = np.reshape(maps, (nky, nkz, 1, ncoils, nmaps))
# compute sensitivity maps (SigPy)
ksp = np.transpose(kspace[:, :, 0, :], [2, 1, 0])
if calib_method is 'espirit':
maps = app.EspiritCalib(ksp,
calib_width=calib_size,
crop=crop_value,
device=device,
show_pbar=False).run()
elif calib_method is 'jsense':
maps = app.JsenseRecon(ksp,
mps_ker_width=6,
ksp_calib_width=calib_size,
device=device,
show_pbar=False).run()
else:
raise ValueError('%s calibration method not implemented...' %
calib_method)
maps = np.reshape(np.transpose(maps, [2, 1, 0]),
(nky, nkz, 1, ncoils, nmaps))
# Convert everything to tensors
kspace_tensor = cplx.to_tensor(kspace).unsqueeze(0)
maps_tensor = cplx.to_tensor(maps).unsqueeze(0)
# Do coil combination using sensitivity maps (PyTorch)
A = T.SenseModel(maps_tensor)
im_tensor = A(kspace_tensor, adjoint=True)
# Convert tensor back to numpy array
image = cplx.to_numpy(im_tensor.squeeze(0))
return image, maps