nifreeze.model.dmri module

class nifreeze.model.dmri.AverageDWIModel(self, dataset: nifreeze.data.dmri.base.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.base.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.

applicable_schemes: frozenset[str] = frozenset({'DSI', 'multi-shell', 'single-shell'})

Acquisition schemes (as labelled by find_shelling_scheme()) the model supports.

excludes_b0: bool = False

Whether b=0 volumes are excluded from fitting/prediction.

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.

requires_multishell: bool = False

Whether the model requires more than one non-zero shell.

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.base.DWI, max_b: float | int | None = None, **kwargs)

Bases: BaseDWIModel

A wrapper of DiffusionKurtosisModel.

Initialization.

Parameters:

dataset (DWI) – Reference to a DWI object.

applicable_schemes: frozenset[str] = frozenset({'multi-shell'})

Acquisition schemes (as labelled by find_shelling_scheme()) the model supports.

requires_multishell: bool = True

Whether the model requires more than one non-zero shell.

class nifreeze.model.dmri.DTIModel(self, dataset: nifreeze.data.dmri.base.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.

applicable_schemes: frozenset[str] = frozenset({'multi-shell', 'single-shell'})

Acquisition schemes (as labelled by find_shelling_scheme()) the model supports.

nifreeze.model.dmri.DWI_DKI_SHELL_ERROR_MSG = 'DKI requires at least 3 b-values (which can include b=0).'

dMRI dataset DKI model insufficient shells error message.

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.base.DWI, max_b: float | int | None = None, **kwargs)

Bases: BaseDWIModel

A wrapper of GaussianProcessModel.

The Gaussian process does not follow the DIPY Model(gtab).fit(data) convention that BaseDWIModel implements: it is constructed from a kernel_model and receives the gradient table at fit time, fitting all voxels at once (vectorized across GP targets). _fit/fit_predict are therefore overridden here rather than reusing the chunked DIPY path.

Initialization.

Parameters:

dataset (DWI) – Reference to a DWI object.

applicable_schemes: frozenset[str] = frozenset({'multi-shell', 'single-shell'})

Acquisition schemes (as labelled by find_shelling_scheme()) the model supports.

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

Fit the GP (LOVO or single-fit) and predict the held-out orientation.

single_fit_is_canary: bool = True

Whether single-fit only makes sense as a self-consistency canary (see _warn_single_fit_canary()).

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

Bases: BaseDWIModel

A wrapper of nifreeze.model.gqi.GeneralizedQSamplingModel.

Initialization.

Parameters:

dataset (DWI) – Reference to a DWI object.

excludes_b0: bool = True

Whether b=0 volumes are excluded from fitting/prediction.

single_fit_is_canary: bool = True

Whether single-fit only makes sense as a self-consistency canary (see _warn_single_fit_canary()).