nifreeze.model.dmri module

class nifreeze.model.dmri.AverageDWIModel(self, dataset: nifreeze.data.dmri.DWI, stat: str = 'median', atol_low: float = 100.0, atol_high: float = 100.0, detrend: bool = False, **kwargs)

Bases: ExpectationModel

A trivial model that returns an average DWI volume.

Implement object initialization.

Parameters:

• dataset (DWI) – Reference to a DWI object.

• stat (str, optional) – Whether the summary statistic to apply is "mean" or "median".

• atol_low (float, optional) – A lower bound for the b-value corresponding to the diffusion weighted images that will be averaged.

• atol_low (float, optional) – An upper bound for the b-value corresponding to the diffusion weighted images that will be averaged.

• detrend (bool, optional) – Whether the overall distribution of each diffusion weighted image will be standardized and centered around the src.nifreeze.model.base.DEFAULT_CLIP_PERCENTILE percentile.

fit_predict(index: int | None = None, *_, **kwargs) → ndarray

Return the average map.

class nifreeze.model.dmri.BaseDWIModel(self, dataset: nifreeze.data.dmri.DWI, max_b: float | int | None = None, **kwargs)

Bases: BaseModel

Interface and default methods for DWI models.

Initialization.

Parameters:

dataset (DWI) – Reference to a DWI object.

fit_predict(index: int | None = None, **kwargs) → ndarray | None

Predict asynchronously chunk-by-chunk the diffusion signal.

Parameters:

index (int) – The volume index that is left-out in fitting, and then predicted.

nifreeze.model.dmri.DEFAULT_S0_CLIP_PERCENTILE = 98

Upper percentile threshold for non-diffusion-weighted signal estimation.

class nifreeze.model.dmri.DKIModel(self, dataset: nifreeze.data.dmri.DWI, max_b: float | int | None = None, **kwargs)

Bases: BaseDWIModel

A wrapper of dipy.reconst.dki.DiffusionKurtosisModel.

Initialization.

Parameters:

dataset (DWI) – Reference to a DWI object.

class nifreeze.model.dmri.DTIModel(self, dataset: nifreeze.data.dmri.DWI, max_b: float | int | None = None, **kwargs)

Bases: BaseDWIModel

A wrapper of dipy.reconst.dti.TensorModel.

Initialization.

Parameters:

dataset (DWI) – Reference to a DWI object.

nifreeze.model.dmri.DWI_GTAB_ERROR_MSG = 'Dataset MUST have a gradient table.'

dMRI gradient table error message.

nifreeze.model.dmri.DWI_OBJECT_ERROR_MSG = 'Dataset MUST be a DWI object.'

dMRI object error message.

nifreeze.model.dmri.DWI_SIZE_ERROR_MSG = 'DWI dataset is too small ({directions} directions).'

dMRI dataset size error message.

class nifreeze.model.dmri.GPModel(self, dataset: nifreeze.data.dmri.DWI, max_b: float | int | None = None, **kwargs)

Bases: BaseDWIModel

A wrapper of GaussianProcessModel.

Initialization.

Parameters:

dataset (DWI) – Reference to a DWI object.

class nifreeze.model.dmri.GQIModel(self, dataset: nifreeze.data.dmri.DWI, max_b: float | int | None = None, **kwargs)

Bases: BaseDWIModel

A wrapper of dipy.reconst.gqi.GeneralizedQSamplingModel.

Initialization.

Parameters:

dataset (DWI) – Reference to a DWI object.