src.standardized package

Submodules

src.standardized.ETP_SRI_LinearFitting module

class src.standardized.ETP_SRI_LinearFitting.ETP_SRI_LinearFitting(bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False)

Bases: OsipiBase

WIP Implementation and execution of the submitted algorithm

accepted_dimensions = 1

id_algorithm_type = 'Linear fit'

id_author = 'Eric T. Peterson, SRI'

id_return_parameters = 'f, D*, D'

id_units = 'seconds per milli metre squared'

ivim_fit(signals, bvalues=None, linear_fit_option=False, **kwargs)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None. linear_fit_option (bool, optional): This fit has an option to only run a linear fit. Defaults to False.

Returns:

_type_: _description_

required_bounds = False

required_bounds_optional = True

required_bvalues = 3

required_initial_guess = False

required_initial_guess_optional = False

required_thresholds = [0, 1]

src.standardized.IAR_LU_biexp module

class src.standardized.IAR_LU_biexp.IAR_LU_biexp(bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False)

Bases: OsipiBase

Bi-exponential fitting algorithm by Ivan A. Rashid, Lund University

accepted_dimensions = 1

id_algorithm_type = 'Bi-exponential fit'

id_author = 'Ivan A. Rashid, LU'

id_return_parameters = 'f, D*, D'

id_units = 'seconds per milli metre squared or milliseconds per micro metre squared'

ivim_fit(signals, bvalues, **kwargs)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.

Returns:

_type_: _description_

required_bounds = False

required_bounds_optional = True

required_bvalues = 4

required_initial_guess = False

required_initial_guess_optional = True

required_thresholds = [0, 0]

src.standardized.IAR_LU_modified_mix module

class src.standardized.IAR_LU_modified_mix.IAR_LU_modified_mix(bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False)

Bases: OsipiBase

Bi-exponential fitting algorithm by Ivan A. Rashid, Lund University

accepted_dimensions = 1

id_algorithm_type = 'Bi-exponential fit'

id_author = 'Ivan A. Rashid, LU'

id_return_parameters = 'f, D*, D'

id_units = 'seconds per milli metre squared or milliseconds per micro metre squared'

ivim_fit(signals, bvalues, **kwargs)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.

Returns:

_type_: _description_

required_bounds = False

required_bounds_optional = True

required_bvalues = 4

required_initial_guess = False

required_initial_guess_optional = True

required_thresholds = [0, 0]

src.standardized.IAR_LU_modified_topopro module

class src.standardized.IAR_LU_modified_topopro.IAR_LU_modified_topopro(bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False)

Bases: OsipiBase

Bi-exponential fitting algorithm by Ivan A. Rashid, Lund University

accepted_dimensions = 1

id_algorithm_type = 'Bi-exponential fit'

id_author = 'Ivan A. Rashid, LU'

id_return_parameters = 'f, D*, D'

id_units = 'seconds per milli metre squared or milliseconds per micro metre squared'

ivim_fit(signals, bvalues, **kwargs)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.

Returns:

_type_: _description_

required_bounds = False

required_bounds_optional = True

required_bvalues = 4

required_initial_guess = False

required_initial_guess_optional = True

required_thresholds = [0, 0]

src.standardized.IAR_LU_segmented_2step module

class src.standardized.IAR_LU_segmented_2step.IAR_LU_segmented_2step(bvalues=None, thresholds=None, bounds=None, initial_guess=None)

Bases: OsipiBase

Bi-exponential fitting algorithm by Ivan A. Rashid, Lund University

accepted_dimensions = 1

id_algorithm_type = 'Segmented bi-exponential fit'

id_author = 'Ivan A. Rashid, LU'

id_return_parameters = 'f, D*, D'

id_units = 'seconds per milli metre squared or milliseconds per micro metre squared'

ivim_fit(signals, bvalues, thresholds=None, **kwargs)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.

Returns:

_type_: _description_

required_bounds = False

required_bounds_optional = True

required_bvalues = 4

required_initial_guess = False

required_initial_guess_optional = True

required_thresholds = [0, 0]

src.standardized.IAR_LU_segmented_3step module

class src.standardized.IAR_LU_segmented_3step.IAR_LU_segmented_3step(bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False)

Bases: OsipiBase

Bi-exponential fitting algorithm by Ivan A. Rashid, Lund University

accepted_dimensions = 1

id_algorithm_type = 'Segmented bi-exponential fit'

id_author = 'Ivan A. Rashid, LU'

id_return_parameters = 'f, D*, D'

id_units = 'seconds per milli metre squared or milliseconds per micro metre squared'

ivim_fit(signals, bvalues, **kwargs)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.

Returns:

_type_: _description_

required_bounds = False

required_bounds_optional = True

required_bvalues = 4

required_initial_guess = False

required_initial_guess_optional = True

required_thresholds = [0, 0]

src.standardized.IAR_LU_subtracted module

class src.standardized.IAR_LU_subtracted.IAR_LU_subtracted(bvalues=None, thresholds=None, bounds=None, initial_guess=None)

Bases: OsipiBase

Bi-exponential fitting algorithm by Ivan A. Rashid, Lund University

accepted_dimensions = 1

id_algorithm_type = 'Segmented bi-exponential fit'

id_author = 'Ivan A. Rashid, LU'

id_return_parameters = 'f, D*, D'

id_units = 'seconds per milli metre squared or milliseconds per micro metre squared'

ivim_fit(signals, bvalues, **kwargs)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.

Returns:

_type_: _description_

required_bounds = False

required_bounds_optional = True

required_bvalues = 4

required_initial_guess = False

required_initial_guess_optional = True

required_thresholds = [0, 0]

src.standardized.OGC_AmsterdamUMC_Bayesian_biexp module

class src.standardized.OGC_AmsterdamUMC_Bayesian_biexp.OGC_AmsterdamUMC_Bayesian_biexp(bvalues=None, thresholds=None, bounds=([0, 0, 0.005, 0.7], [0.005, 0.7, 0.2, 1.3]), initial_guess=None, fitS0=True, prior_in=None)

Bases: OsipiBase

Bayesian Bi-exponential fitting algorithm by Oliver Gurney-Champion, Amsterdam UMC

accepted_dimensions = 1

accepts_priors = True

id_algorithm_type = 'Bi-exponential fit'

id_author = 'Oliver Gurney Champion, Amsterdam UMC'

id_return_parameters = 'f, D*, D, S0'

id_units = 'seconds per milli metre squared or milliseconds per micro metre squared'

initialize(bounds=([0, 0, 0.005, 0.7], [0.005, 0.7, 0.2, 1.3]), initial_guess=None, fitS0=True, prior_in=None)

Placeholder for subclass initialization

ivim_fit(signals, bvalues, initial_guess=None, **kwargs)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.

Returns:

_type_: _description_

required_bounds = False

required_bounds_optional = True

required_bvalues = 4

required_initial_guess = False

required_initial_guess_optional = True

required_thresholds = [0, 0]

src.standardized.OGC_AmsterdamUMC_biexp module

class src.standardized.OGC_AmsterdamUMC_biexp.OGC_AmsterdamUMC_biexp(bvalues=None, thresholds=None, bounds=([0, 0, 0.005, 0.7], [0.005, 0.7, 0.2, 1.3]), initial_guess=None, fitS0=False)

Bases: OsipiBase

Bi-exponential fitting algorithm by Oliver Gurney-Champion, Amsterdam UMC

accepted_dimensions = 1

id_algorithm_type = 'Bi-exponential fit'

id_author = 'Oliver Gurney Champion, Amsterdam UMC'

id_return_parameters = 'f, D*, D, S0'

id_units = 'seconds per milli metre squared or milliseconds per micro metre squared'

initialize(bounds, initial_guess, fitS0)

Placeholder for subclass initialization

ivim_fit(signals, bvalues, initial_guess=None, **kwargs)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.

Returns:

_type_: _description_

required_bounds = False

required_bounds_optional = True

required_bvalues = 4

required_initial_guess = False

required_initial_guess_optional = True

required_thresholds = [0, 0]

src.standardized.OGC_AmsterdamUMC_biexp_segmented module

class src.standardized.OGC_AmsterdamUMC_biexp_segmented.OGC_AmsterdamUMC_biexp_segmented(bvalues=None, thresholds=150, bounds=([0, 0, 0.005], [0.005, 0.7, 0.2]), initial_guess=[0.001, 0.01, 0.01, 1])

Bases: OsipiBase

Segmented bi-exponential fitting algorithm by Oliver Gurney-Champion, Amsterdam UMC

accepted_dimensions = 1

id_algorithm_type = 'Segmented bi-exponential fit'

id_author = 'Oliver Gurney Champion, Amsterdam UMC'

id_return_parameters = 'f, D*, D, S0'

id_units = 'seconds per milli metre squared or milliseconds per micro metre squared'

initialize(bounds, initial_guess, thresholds=300)

Placeholder for subclass initialization

ivim_fit(signals, bvalues, initial_guess=None, **kwargs)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.

Returns:

_type_: _description_

required_bounds = False

required_bounds_optional = True

required_bvalues = 4

required_initial_guess = False

required_initial_guess_optional = True

required_thresholds = [1, 1]

src.standardized.OJ_GU_seg module

class src.standardized.OJ_GU_seg.OJ_GU_seg(bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False)

Bases: OsipiBase

Segmented fitting algorithm by Oscar Jalnefjord, University of Gothenburg

accepted_dimensions = 1

id_algorithm_type = 'Segmented bi-exponential fit'

id_author = 'Oscar Jalnefjord, GU'

id_return_parameters = 'f, D*, D'

id_units = 'mm2/s'

ivim_fit(signals, bvalues=None)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.

Returns:

_type_: _description_

required_bounds = False

required_bounds_optional = False

required_bvalues = 4

required_initial_guess = False

required_initial_guess_optional = False

required_thresholds = [0, 0]

src.standardized.PV_MUMC_biexp module

class src.standardized.PV_MUMC_biexp.PV_MUMC_biexp(bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False)

Bases: OsipiBase

Bi-exponential fitting algorithm by Paulien Voorter, Maastricht University

accepted_dimensions = 1

id_algorithm_type = 'Bi-exponential fit'

id_author = 'Paulien Voorter MUMC'

id_return_parameters = 'f, D*, D'

id_units = 'seconds per milli metre squared or milliseconds per micro metre squared'

ivim_fit(signals, bvalues=None)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.

Returns:

_type_: _description_

required_bounds = False

required_bounds_optional = True

required_bvalues = 4

required_initial_guess = False

required_initial_guess_optional = True

required_thresholds = [0, 0]

src.standardized.PvH_KB_NKI_IVIMfit module

class src.standardized.PvH_KB_NKI_IVIMfit.PvH_KB_NKI_IVIMfit(bvalues=None, thresholds=None, bounds=None, initial_guess=None)

Bases: OsipiBase

Bi-exponential fitting algorithm by Petra van Houdt and Koen Baas, NKI

accepted_dimensions = 1

id_algorithm_type = 'Bi-exponential fit'

id_author = 'Group Uulke van der Heide, NKI'

id_return_parameters = 'f, D*, D'

id_units = 'seconds per milli metre squared or milliseconds per micro metre squared'

ivim_fit(signals, bvalues=None)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.

Returns:

_type_: _description_

required_bounds = False

required_bounds_optional = False

required_bvalues = 4

required_initial_guess = False

required_initial_guess_optional = False

required_thresholds = [0, 0]

Module contents