src.standardized package

Submodules

src.standardized.ASD_MemorialSloanKettering_QAMPER_IVIM module

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

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]

supported_bounds = False

supported_dimensions = 1

supported_initial_guess = False

supported_priors = False

supported_thresholds = True

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

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_

ivim_fit_full_volume(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]

supported_bounds = True

supported_dimensions = 1

supported_initial_guess = True

supported_priors = False

supported_thresholds = False

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

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]

supported_bounds = True

supported_dimensions = 1

supported_initial_guess = False

supported_priors = False

supported_thresholds = False

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

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]

supported_bounds = True

supported_dimensions = 1

supported_initial_guess = False

supported_priors = False

supported_thresholds = False

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

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]

supported_bounds = True

supported_dimensions = 1

supported_initial_guess = True

supported_priors = False

supported_thresholds = True

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

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]

supported_bounds = True

supported_dimensions = 1

supported_initial_guess = True

supported_priors = False

supported_thresholds = False

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

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]

supported_bounds = True

supported_dimensions = 1

supported_initial_guess = True

supported_priors = False

supported_thresholds = False

src.standardized.IVIM_NEToptim module

class src.standardized.IVIM_NEToptim.Arg

Bases: object

class src.standardized.IVIM_NEToptim.IVIM_NEToptim(SNR=None, bvalues=None, thresholds=None, bounds=None, initial_guess=None, fitS0=True, traindata=None, n=5000000)

Bases: OsipiBase

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

accepted_dimensions = 1

id_algorithm_type = 'Deep learnt 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, traindata, SNR, n)

Placeholder for subclass initialization

ivim_fit(signals, bvalues, **kwargs)

Perform the IVIM fit

Args:

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

Returns:

_type_: _description_

ivim_fit_full_volume(signals, bvalues, retrain_on_input_data=False, **kwargs)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like): 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 = False

required_thresholds = [0, 0]

reshape_to_voxelwise(data)

reshapes multi-D input (spatial dims, bvvalue) data to 2D voxel-wise array Args:

data (array): mulit-D array (data x b-values)

Returns:

out (array): 2D array (voxel x b-value)

supported_bounds = True

supported_initial_guess = False

supported_thresholds = False

training_data(bvalues, data=None, SNR=(5, 100), n=5000000, Drange=(0.0003, 0.0035), frange=(0, 1), Dprange=(0.005, 0.12), rician_noise=False)

class src.standardized.IVIM_NEToptim.NetArgs

Bases: object

class src.standardized.IVIM_NEToptim.NetPars

Bases: object

src.standardized.OGC_AmsterdamUMC_Bayesian_biexp module

class src.standardized.OGC_AmsterdamUMC_Bayesian_biexp.OGC_AmsterdamUMC_Bayesian_biexp(bvalues=None, thresholds=None, bounds=None, initial_guess=None, fitS0=True, prior_in=None)

Bases: OsipiBase

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

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=None, initial_guess=None, fitS0=True, prior_in=None, thresholds=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_

ivim_fit_full_volume(signals, bvalues, njobs=4, **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, 1]

reshape_to_voxelwise(data)

reshapes multi-D input (spatial dims, bvvalue) data to 2D voxel-wise array Args:

data (array): mulit-D array (data x b-values)

Returns:

out (array): 2D array (voxel x b-value)

supported_bounds = True

supported_dimensions = 1

supported_initial_guess = True

supported_priors = True

supported_thresholds = True

src.standardized.OGC_AmsterdamUMC_biexp module

class src.standardized.OGC_AmsterdamUMC_biexp.OGC_AmsterdamUMC_biexp(bvalues=None, thresholds=None, bounds=None, initial_guess=None, fitS0=True)

Bases: OsipiBase

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

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, **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]

supported_bounds = True

supported_dimensions = 1

supported_initial_guess = True

supported_priors = False

supported_thresholds = False

src.standardized.OGC_AmsterdamUMC_biexp_segmented module

class src.standardized.OGC_AmsterdamUMC_biexp_segmented.OGC_AmsterdamUMC_biexp_segmented(bvalues=None, thresholds=150, bounds=None, initial_guess=None)

Bases: OsipiBase

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

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)

Placeholder for subclass initialization

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 = [1, 1]

supported_bounds = True

supported_dimensions = 1

supported_initial_guess = True

supported_priors = False

supported_thresholds = True

src.standardized.OJ_GU_bayesMATLAB module

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

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]

supported_bounds = False

supported_dimensions = 1

supported_initial_guess = False

supported_priors = False

supported_thresholds = True

src.standardized.OJ_GU_segMATLAB module

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

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]

supported_bounds = True

supported_dimensions = 1

supported_initial_guess = False

supported_priors = False

supported_thresholds = True

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

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]

supported_bounds = False

supported_dimensions = 1

supported_initial_guess = False

supported_priors = False

supported_thresholds = False

src.standardized.Super_IVIM_DC module

class src.standardized.Super_IVIM_DC.Super_IVIM_DC(bvalues=None, thresholds=None, bounds=None, initial_guess=None, fitS0=True, SNR=None)

Bases: OsipiBase

Supervised deep learnt fitting algorithm by Moti Freiman and Noam Korngut, TechnionIIT

accepted_dimensions = 1

id_algorithm_type = 'Supervised Deep learnt bi-exponential fit with data consistency'

id_author = 'Moti Freiman and Noam Korngut, TechnIIT'

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, SNR, working_dir='/home/runner/work/TF2.4_IVIM-MRI_CodeCollection/TF2.4_IVIM-MRI_CodeCollection/docs', ivimnet_filename='ivimnet', super_ivim_dc_filename='super_ivim_dc')

Placeholder for subclass initialization

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:

results: a dictionary containing “d”, “f”, and “Dp”.

ivim_fit_full_volume(signals, bvalues, retrain_on_input_data=False, **kwargs)

Perform the IVIM fit

Args:

signals (array-like) bvalues (array-like): 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]

reshape_to_voxelwise(data)

reshapes multi-D input (spatial dims, bvvalue) data to 2D voxel-wise array Args:

data (array): mulit-D array (data x b-values)

Returns:

out (array): 2D array (voxel x b-value)

supported_bounds = True

supported_initial_guess = True

supported_thresholds = False

src.standardized.TCML_TechnionIIT_SLS module

class src.standardized.TCML_TechnionIIT_SLS.TCML_TechnionIIT_SLS(bvalues=None, thresholds=None, bounds=None, initial_guess=None, fitS0=True)

Bases: OsipiBase

TCML_TechnionIIT_lsqlm fitting algorithm by Angeleene Ang, Moti Freiman and Noam Korngut, TechnionIIT

accepted_dimensions = 1

id_algorithm_type = 'Bi-exponential fit, segmented fitting'

id_author = 'Angeleene Ang, Moti Freiman and Noam Korngut, TechnIIT'

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

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

initialize(bounds, fitS0, thresholds)

Placeholder for subclass initialization

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 = False

required_thresholds = [0, 1]

supported_bounds = True

supported_initial_guess = False

supported_thresholds = True

src.standardized.TCML_TechnionIIT_lsqBOBYQA module

class src.standardized.TCML_TechnionIIT_lsqBOBYQA.TCML_TechnionIIT_lsqBOBYQA(bvalues=None, thresholds=None, bounds=None, initial_guess=None, fitS0=True)

Bases: OsipiBase

TCML_TechnionIIT_lsqBOBYQA fitting algorithm by Moti Freiman and Noam Korngut, TechnionIIT

accepted_dimensions = 1

id_algorithm_type = 'Bi-exponential fit, BOBYQO'

id_author = 'Moti Freiman and Noam Korngut, TechnIIT'

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, **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]

supported_bounds = True

supported_initial_guess = True

supported_thresholds = False

src.standardized.TCML_TechnionIIT_lsq_sls_BOBYQA module

class src.standardized.TCML_TechnionIIT_lsq_sls_BOBYQA.TCML_TechnionIIT_lsq_sls_BOBYQA(bvalues=None, thresholds=None, bounds=None, initial_guess=None, fitS0=True)

Bases: OsipiBase

TCML_TechnionIIT_lsqlm fitting algorithm by Angeleene Ang, Moti Freiman and Noam Korngut, TechnionIIT

accepted_dimensions = 1

id_algorithm_type = 'Bi-exponential fit, SLS fit followed by Trust Region Reflective algorithm'

id_author = 'Angeleene Ang, Moti Freiman and Noam Korngut, TechnIIT'

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

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

initialize(bounds, fitS0, thresholds)

Placeholder for subclass initialization

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 = False

required_thresholds = [0, 1]

supported_bounds = True

supported_initial_guess = False

supported_thresholds = True

src.standardized.TCML_TechnionIIT_lsq_sls_lm module

class src.standardized.TCML_TechnionIIT_lsq_sls_lm.TCML_TechnionIIT_lsq_sls_lm(bvalues=None, thresholds=None, bounds=None, initial_guess=None, fitS0=True)

Bases: OsipiBase

TCML_TechnionIIT_lsqlm fitting algorithm by Angeleene Ang, Moti Freiman and Noam Korngut, TechnionIIT

accepted_dimensions = 1

id_algorithm_type = 'Bi-exponential, segmented as initaition, followed by Levenberg-Marquardt algorithm'

id_author = 'Angeleene Ang, Moti Freiman and Noam Korngut, TechnIIT'

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

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

initialize(bounds, fitS0, thresholds)

Placeholder for subclass initialization

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 = False

required_thresholds = [0, 1]

supported_bounds = True

supported_initial_guess = False

supported_thresholds = True

src.standardized.TCML_TechnionIIT_lsq_sls_trf module

class src.standardized.TCML_TechnionIIT_lsq_sls_trf.TCML_TechnionIIT_lsq_sls_trf(bvalues=None, thresholds=None, bounds=None, initial_guess=None, fitS0=True)

Bases: OsipiBase

TCML_TechnionIIT_lsqlm fitting algorithm by Angeleene Ang, Moti Freiman and Noam Korngut, TechnionIIT

accepted_dimensions = 1

id_algorithm_type = 'Bi-exponential fit, SLS fit followed by Trust Region Reflective algorithm'

id_author = 'Angeleene Ang, Moti Freiman and Noam Korngut, TechnIIT'

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

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

initialize(bounds, fitS0, thresholds)

Placeholder for subclass initialization

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 = False

required_thresholds = [0, 1]

supported_bounds = True

supported_initial_guess = False

supported_thresholds = True

src.standardized.TCML_TechnionIIT_lsqlm module

class src.standardized.TCML_TechnionIIT_lsqlm.TCML_TechnionIIT_lsqlm(bvalues=None, thresholds=None, bounds=None, initial_guess=None, fitS0=True)

Bases: OsipiBase

TCML_TechnionIIT_lsqlm fitting algorithm by Angeleene Ang, Moti Freiman and Noam Korngut, TechnionIIT

accepted_dimensions = 1

id_algorithm_type = 'Bi-exponential fit with Levenberg-Marquardt algorithm'

id_author = 'Angeleene Ang, Moti Freiman and Noam Korngut, TechnIIT'

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, **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]

supported_bounds = True

supported_initial_guess = True

supported_thresholds = False

src.standardized.TCML_TechnionIIT_lsqtrf module

class src.standardized.TCML_TechnionIIT_lsqtrf.TCML_TechnionIIT_lsqtrf(bvalues=None, thresholds=None, bounds=None, initial_guess=None, fitS0=True)

Bases: OsipiBase

TCML_TechnionIIT_lsqlm fitting algorithm by Angeleene Ang, Moti Freiman and Noam Korngut, TechnionIIT

accepted_dimensions = 1

id_algorithm_type = 'Bi-exponential fit, Trust Region Reflective algorithm'

id_author = 'Angeleene Ang, Moti Freiman and Noam Korngut, TechnIIT'

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, **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]

supported_bounds = True

supported_initial_guess = True

supported_thresholds = False

src.standardized.TF_reference_IVIMfit module

class src.standardized.TF_reference_IVIMfit.TF_reference_IVIMfit(bvalues=None, thresholds=200, bounds=None, initial_guess=None)

Bases: OsipiBase

Bi-exponential fitting algorithm by IVIM Task force

accepted_dimensions = 1

id_algorithm_type = 'Bi-exponential fit'

id_author = 'OSIPI IVIM TF'

id_return_parameters = 'f, D*, D'

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

initialize(bounds, thresholds)

Placeholder for subclass initialization

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 = False

required_thresholds = [0, 1]

supported_bounds = True

supported_initial_guess = False

supported_thresholds = True

Module contents