deepmr.io.read_acqheader

deepmr.io.read_acqheader(filepath, *args, device='cpu', verbose=False, **kwargs)

Read acquisition header from file.

The header info (e.g., k-space trajectory, shape) can be used to simulate acquisitions or to inform raw data loading (e.g., via ordering) to reshape from acquisition to reconstruction ordering and image post-processing (transposition, flipping) and exporting.

Parameters:

• filepath (str) – Path to acquisition header file. Supports wildcard (e.g., /path-to-data-folder/*.h5).

• *args – Variable length argument list passed to the specific subroutines for the different datatypes (see Keyword Arguments).

• device (str, optional) – Computational device for internal attributes. The default is cpu.

• verbose (int, optional) – Verbosity level (0=Silent, 1=Less, 2=More). The default is 0.

Keyword Arguments:

• dcfpath (str, optional) – Path to the dcf file (deepmr.io.matlab.read_matlab_acqhead()). The default is None.

• methodpath (str, optional) – Path to the schedule description file (deepmr.io.matlab.read_matlab_acqhead()). The default is None.

• sliceprofpath (str, optional) – Path to the slice profile file (deepmr.io.matlab.read_matlab_acqhead()). The default is None.

Returns:

head – Deserialized acquisition header.

Return type:

Header

Notes

The returned head (deepmr.Header()) is a structure with the following fields:

• shape (torch.Tensor):

  This is the expected image size of shape (nz, ny, nx).

• resolution (torch.Tensor):

  This is the expected image resolution in mm of shape (dz, dy, dx).

• t (torch.Tensor):

  This is the readout sampling time (0, t_read) in ms. with shape (nsamples,).

• traj (torch.Tensor):

  This is the k-space trajectory normalized as (-0.5 * shape, 0.5 * shape) with shape (ncontrasts, nviews, nsamples, ndims).

• dcf (torch.Tensor):

  This is the k-space sampling density compensation factor with shape (ncontrasts, nviews, nsamples).

• FA (torch.Tensor, float):

  This is either the acquisition flip angle in degrees or the list of flip angles of shape (ncontrasts,) for each image in the series.

• TR (torch.Tensor, float):

  This is either the repetition time in ms or the list of repetition times of shape (ncontrasts,) for each image in the series.

• TE (torch.Tensor, float):

  This is either the echo time in ms or the list of echo times of shape (ncontrasts,) for each image in the series.

• TI (torch.Tensor, float):

  This is either the inversion time in ms or the list of inversion times of shape (ncontrasts,) for each image in the series.

• user (dict):

  User parameters. Some examples are:

  • ordering (torch.Tensor):

    Indices for reordering (acquisition to reconstruction) of acquired k-space data, shaped (3, nslices * ncontrasts * nview), whose rows are contrast_index, slice_index and view_index, respectively.

  • mode (str):

    Acquisition mode (2Dcart, 3Dcart, 2Dnoncart, 3Dnoncart).

  • separable (bool):

    Whether the acquisition can be decoupled by fft along slice / readout directions (3D stack-of-noncartesian / 3D cartesian, respectively) or not (3D noncartesian and 2D acquisitions).

  • slice_profile (torch.Tensor):

    Flip angle scaling along slice profile of shape (nlocs,).

  • basis (torch.Tensor):

    Low rank subspace basis for subspace reconstruction of shape (ncontrasts, ncoeff).

• affine (np.ndarray):

  Affine matrix describing image spacing, orientation and origin of shape (4, 4).

• ref_dicom (pydicom.Dataset):

  Template dicom for image export.

• flip (list):

  List of spatial axis to be flipped after image reconstruction. The default is an empty list (no flipping).

• transpose (list):

  Permutation of image dimensions after reconstruction, depending on acquisition mode:

  • 2Dcart: reconstructed image has (nslices, ncontrasts, ny, nx) -> transpose = [1, 0, 2, 3]

  • 2Dnoncart: reconstructed image has (nslices, ncontrasts, ny, nx) -> transpose = [1, 0, 2, 3]

  • 3Dcart: reconstructed image has (ncontrasts, nz, ny, nx) -> transpose = [0, 1, 2, 3]

  • 3Dnoncart: reconstructed image has (nx, ncontrasts, nz, ny) -> transpose = [1, 2, 3, 0]

  The default is an empty list (no transposition).