snake.mrd_utils.loader

Loader of MRD data.

Module Contents

Classes

MRDLoader	Base class for MRD data loader.
CartesianFrameDataLoader	Load cartesian MRD files k-space frames iteratively.
NonCartesianFrameDataLoader	Non Cartesian Dataloader.

Functions

read_mrd_header	Read the header of the MRD file.
parse_sim_conf	Parse the header to populate SimConfig from an MRD Header.
parse_waveform_information	Parse the waveform information from the MRD file.
get_affine_from_image	Extract the affine matrix from the header of an MRD.Image.

Data

log
GenericPath

API

snake.mrd_utils.loader.log = 'getLogger(...)'

snake.mrd_utils.loader.GenericPath = None

snake.mrd_utils.loader.read_mrd_header(filename: snake.mrd_utils.loader.GenericPath | ismrmrd.Dataset) → ismrmrd.xsd.ismrmrdHeader

Read the header of the MRD file.

class snake.mrd_utils.loader.MRDLoader(filename: snake.mrd_utils.loader.GenericPath, dataset_name: str = 'dataset', writeable: bool = False, swmr: bool = False)

Bases: snake._meta.LogMixin

Base class for MRD data loader.

This is to be used as a context manager.

It reimplements most of the methods of the mrd.Dataset class, and adds some useful wrappers. With this dataloader, you can open the dataset in readonly mode, which is not possible with mrd.

Initialization

__enter__()

__exit__(exc_type: Any, exc_value: Any, traceback: Any)

iter_frames(start: int | None = None, stop: int | None = None, step: int | None = None, shot_dim: bool = False) → collections.abc.Generator[tuple[int, numpy.typing.NDArray[numpy.float32], numpy.typing.NDArray[numpy.complex64]], None, None]

Iterate over kspace frames of the dataset.

Parameters:

• start (int, optional) – Start index of the iteration.

• stop (int, optional) – Stop index of the iteration.

• step (int, optional) – Step of the iteration.

• shot_dim (bool, optional) – Return the data reshaped with the shot dimension first.

Yields:

tuple[int, np.ndarray, np.ndarray] – The index of the frame, the trajectory and the kspace data.

_read_xml_header() → ismrmrd.xsd.ismrmrdHeader

Read the header of the MRD file.

_read_waveform(wavnum: int) → ismrmrd.Waveform

_read_image(impath: str, imnum: int = 0) → ismrmrd.Image

header() → ismrmrd.xsd.ismrmrdHeader

Get the header from the mrd file.

property _dataset: h5py.Dataset

Get MRD dataset.

property n_frames: int

Number of frames.

property shape: tuple[int, ...]

Shape of the volume.

property n_coils: int

Number of coils.

property n_acquisition: int

Number of acquisition in the dataset.

__len__()

property n_sample: int

Number of samples in a single acquisition.

property n_shots: int

Number of samples in a single acquisition.

Notes

for EPI this is the number of phase encoding lines in the EPI zigzag.

property engine_model: str

Get the engine model.

property slice_2d: bool

Is the acquisition run on 2D slices.

get_phantom(imnum: int = 0) → snake.core.Phantom

Load the phantom from the dataset.

_all_waveform_infos() → dict[int, dict]

get_dynamic(waveform_num: int) → snake.core.DynamicData

Get dynamic data.

get_all_dynamic() → list[snake.core.DynamicData]

Get all dynamic data.

get_sim_conf() → snake.core.SimConfig

Parse the sim config.

_get_image_data(name: str, idx: int = 0) → numpy.typing.NDArray[numpy.complex64] | None

get_smaps(resample: bool = True) → numpy.typing.NDArray[numpy.complex64] | None

Load the sensitivity maps from the dataset.

Parameters:

• resample (bool) – If resample is True (default), the sensitivity maps are resampled using the affine transformation

• sim_conf. (describe in the phantom and)

Returns:

• None (if no Smaps is found)

• NDArray (The coils sensitivity maps)

get_coil_cov() → numpy.typing.NDArray | None

Load the coil covariance from the dataset.

class snake.mrd_utils.loader.CartesianFrameDataLoader(filename: snake.mrd_utils.loader.GenericPath, dataset_name: str = 'dataset', writeable: bool = False, swmr: bool = False)

Bases: snake.mrd_utils.loader.MRDLoader

Load cartesian MRD files k-space frames iteratively.

Parameters:

filename (source for the MRD file.)

Examples

>>> for mask, kspace in CartesianFrameDataLoader("test.mrd"):
        image = ifft(kspace)

Initialization

get_kspace_frame(idx: int) → tuple[numpy.ndarray, numpy.ndarray]

Get the k-space frame.

class snake.mrd_utils.loader.NonCartesianFrameDataLoader(filename: snake.mrd_utils.loader.GenericPath, dataset_name: str = 'dataset', writeable: bool = False, swmr: bool = False)

Bases: snake.mrd_utils.loader.MRDLoader

Non Cartesian Dataloader.

Iterate over the acquisition of the MRD file.

Examples

>>> from mrinufft import get_operator
>>> dataloader =  NonCartesianFrameDataLoader("test.mrd")
>>> for mask, kspace in data_loader:
...     nufft = get_operator("finufft")(traj,
...     shape=dataloader.shape, n_coils=dataloader.n_coils)
...     image = nufft.adj_op(kspace)

Initialization

get_kspace_frame(idx: int, shot_dim: bool = False) → tuple[numpy.typing.NDArray[numpy.float32], numpy.typing.NDArray[numpy.complex64]]

Get the k-space frame and the associated trajectory.

Parameters:

• idx (int) – Index of the frame to get.

• shot_dim (bool, optional) – Return the data reshaped with the shot dimension first.

Returns:

• np.ndarray – The trajectory.

• np.ndarray – The kspace data.

snake.mrd_utils.loader.parse_sim_conf(header: ismrmrd.xsd.ismrmrdHeader) → snake.core.SimConfig

Parse the header to populate SimConfig from an MRD Header.

snake.mrd_utils.loader.parse_waveform_information(hdr: ismrmrd.xsd.ismrmrdHeader) → dict[int, dict]

Parse the waveform information from the MRD file.

Returns a dictionary with id as key and waveform information (name, parameters, etc.. ) as value.

Base64 encoded parameters are decoded.

snake.mrd_utils.loader.get_affine_from_image(image: ismrmrd.Image) → numpy.typing.NDArray[numpy.float32]

Extract the affine matrix from the header of an MRD.Image.