MRI Reconstruction Data Classes#

class atommic.collections.reconstruction.data.mri_reconstruction_loader.ReconstructionMRIDataset(*args: Any, **kwargs: Any)[source]#

Bases: MRIDataset

A dataset class for accelerated MRI reconstruction.

Examples

>>> from atommic.collections.reconstruction.data.mri_reconstruction_loader import ReconstructionMRIDataset
>>> dataset = ReconstructionMRIDataset(root='data/train', sample_rate=0.1)
>>> print(len(dataset))
100
>>> kspace, coil_sensitivities, mask, initial_prediction, target, attrs, filename, slice_num = dataset[0]
>>> print(kspace.shape)
np.array([30, 640, 368])

Note

Extends atommic.collections.common.data.mri_loader.MRIDataset.

class atommic.collections.reconstruction.data.mri_reconstruction_loader.CC359ReconstructionMRIDataset(*args: Any, **kwargs: Any)[source]#

Bases: Dataset

Supports the CC359 dataset for accelerated MRI reconstruction.

Note

Similar to atommic.collections.common.data.mri_loader.MRIDataset. It does not extend it because we need to override the __init__ and __getitem__ methods.

get_consecutive_slices(data: Dict, key: str, dataslice: int) → numpy.ndarray[source]#

Get consecutive slices from a given data dictionary.

Parameters

  • data (dict) – Data to extract slices from.

  • key (str) – Key to extract slices from.

  • dataslice (int) – Slice to index.

Returns

Array of consecutive slices. If self.consecutive_slices is > 1, then the array will have shape (self.consecutive_slices, *data[key].shape[1:]). Otherwise, the array will have shape data[key].shape[1:].

Return type

np.ndarray

Examples

>>> data = {"kspace": np.random.rand(10, 640, 368)}
>>> from atommic.collections.common.data.mri_loader import MRIDataset
>>> MRIDataset.get_consecutive_slices(data, "kspace", 1).shape
(1, 640, 368)
>>> MRIDataset.get_consecutive_slices(data, "kspace", 5).shape
(5, 640, 368)
class atommic.collections.reconstruction.data.mri_reconstruction_loader.SKMTEAReconstructionMRIDataset(*args: Any, **kwargs: Any)[source]#

Bases: MRIDataset

Supports the SKM-TEA dataset for accelerated MRI reconstruction.

Note

Extends atommic.collections.reconstruction.data.mri_reconstruction_loader.ReconstructionMRIDataset.

class atommic.collections.reconstruction.data.mri_reconstruction_loader.StanfordKneesReconstructionMRIDataset(*args: Any, **kwargs: Any)[source]#

Bases: MRIDataset

Supports the Stanford Knees 2019 dataset for accelerated MRI reconstruction.

Note

Extends atommic.collections.reconstruction.data.mri_reconstruction_loader.ReconstructionMRIDataset.