deepmr.recon.recon_lstsq#
- deepmr.recon.recon_lstsq(data, head, mask=None, niter=1, prior=None, prior_ths=0.01, prior_params=None, solver_params=None, lamda=0.0, stepsize=1.0, basis=None, nsets=1, device=None, cal_data=None, toeplitz=True, use_dcf=True)[source]#
Classical MR reconstruction.
- Parameters:
data (np.ndarray | torch.Tensor) – Input k-space data of shape
(nslices, ncoils, ncontrasts, nviews, nsamples)
.head (deepmr.Header) – DeepMR acquisition header, containing
traj
,shape
anddcf
.mask (np.ndarray | torch.Tensor, optional) – Sampling mask for Cartesian imaging. Expected shape is
(ncontrasts, nviews, nsamples)
. The default isNone
.niter (int, optional) – Number of recon iterations. If single iteration, perform simple zero-filled recon. The default is
1
.prior (str | deepinv.optim.Prior, optional) –
Prior for image regularization. If string, it must be one of the following:
"L1Wav"
: L1 Wavelet regularization."TV"
: Total Variation regularization.
The default is
None
(no regularizer).prior_ths (float, optional) – Threshold for denoising in regularizer. The default is
0.01
.prior_params (dict, optional) – Parameters for Prior initializations. See
deepmr.prox()
. The defaul itNone
(use each regularizer default parameters).solver_params (dict, optional) – Parameters for Solver initializations. See
deepmr.optim()
. The defaul itNone
(use each solver default parameters).lamda (float, optional) – Regularization strength. If 0.0, do not apply regularization. The default is
0.0
.stepsize (float, optional) – Iterations step size. If not provided, estimate from Encoding operator maximum eigenvalue. The default is
None
.basis (np.ndarray | torch.Tensor, optional) – Low rank subspace basis of shape
(ncontrasts, ncoeffs)
. The default isNone
.nsets (int, optional) – Number of coil sensitivity sets of maps. The default is ``1.
device (str, optional) – Computational device. The default is
None
(same asdata
).cal_data (np.ndarray | torch.Tensor, optional) – Calibration dataset for coil sensitivity estimation. The default is
None
(use center region ofdata
).toeplitz (bool, optional) – Use Toeplitz approach for normal equation. The default is
True
.use_dcf (bool, optional) – Use dcf to accelerate convergence. The default is
True
.
- Returns:
Reconstructed image of shape:
- Return type:
img np.ndarray | torch.Tensor