deepmr.recon.EncodingOp#
- deepmr.recon.EncodingOp(data, mask=None, traj=None, dcf=None, shape=None, nsets=1, basis=None, device=None, cal_data=None, toeplitz=False)[source]#
Prepare MR encoding operator for Cartesian / Non-Cartesian imaging.
- Parameters:
data (np.ndarray | torch.Tensor) – Input k-space data of shape
(nslices, ncoils, ncontrasts, nviews, nsamples)
.mask (np.ndarray | torch.Tensor, optional) – Sampling mask for Cartesian imaging. Expected shape is
(ncontrasts, nviews, nsamples)
. The default isNone
.traj (np.ndarray | torch.Tensor, optional) – K-space trajectory for Non Cartesian imaging. Expected shape is
(ncontrasts, nviews, nsamples, ndims)
. The default isNone
.dcf (np.ndarray | torch.Tensor, optional) – K-space density compensation. Expected shape is
(ncontrasts, nviews, nsamples)
. The default isNone
.shape (Iterable[int], optional) – Cartesian grid size of shape
(nz, ny, nx)
. The default isNone
.nsets (int, optional) – Number of coil sensitivity sets of maps. The default is ``1.
basis (np.ndarray | torch.Tensor, optional) – Low rank subspace basis of shape
(ncontrasts, ncoeffs)
. The default isNone
.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
False
.
- Returns:
E (deepmr.linops.Linop) – MR encoding operator (i.e., ksp = E(img)).
EHE (deepmr.linops.NormalLinop) – MR normal operator (i.e., img_n = EHE(img_n-1)).