diff --git a/.travis.yml b/.travis.yml index 0a8f49ac..be0d2e2a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -55,7 +55,7 @@ install: - python setup.py install --record installed_files.txt script: -- flake8 --ignore N802,N806,E731,F401,W504 `find . -name \*.py | grep -v setup.py | grep -v /doc/` +- flake8 --ignore N802,N806,E731,F401,W504,W605 `find . -name \*.py | grep -v setup.py | grep -v /doc/` - nosetests --with-coverage -v #- travis-sphinx build -s doc #- for a in examples/*ipynb; do diff --git a/README.md b/README.md index 6aa8051f..55c7f2f6 100644 --- a/README.md +++ b/README.md @@ -34,7 +34,7 @@ Dmipy allows the user to do Microstructure Imaging research at the highest level - clone repository - python setup.py install -See solutions to [common issues](https://github.com/AthenaEPI/mipy/blob/master/common_issues.md) +See solutions to [common issues](https://github.com/AthenaEPI/dmipy/blob/master/common_issues.md) ## Dependencies Recommended to use Anaconda Python distribution. - numpy >= 1.13 @@ -47,47 +47,48 @@ Recommended to use Anaconda Python distribution. ## Getting Started To get a feeling for how to use Dmipy, we provide a few tutorial notebooks: -- [Setting up an acquisition scheme](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/tutorial_setting_up_acquisition_scheme.ipynb) -- [Simulating and fitting data using a simple Stick model](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/tutorial_simulating_and_fitting_using_a_simple_model.ipynb) -- [Combining biophysical models into a Microstructure model](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/tutorial_combining_biophysical_models_into_microstructure_model.ipynb) +- [Setting up an acquisition scheme](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/tutorial_setting_up_acquisition_scheme.ipynb) +- [Simulating and fitting data using a simple Stick model](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/tutorial_simulating_and_fitting_using_a_simple_model.ipynb) +- [Combining biophysical models into a Microstructure model](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/tutorial_combining_biophysical_models_into_microstructure_model.ipynb) - [Creating a dispersed and/or distributed axon bundle representation](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/tutorial_distributed_model_representations.ipynb) -- [Imposing parameter links and constraints](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/tutorial_imposing_parameter_links.ipynb) -- [Parameter Cascading: Using a simple model to initialize a complex one](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/tutorial_parameter_cascading_and_simulating_nd_datasets.ipynb) +- [Imposing parameter links and constraints](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/tutorial_imposing_parameter_links.ipynb) +- [Parameter Cascading: Using a simple model to initialize a complex one](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/tutorial_parameter_cascading_and_simulating_nd_datasets.ipynb) ## Explanations and Illustrations of Dmipy Contents ### Biophysical Models and Distributions -- [Cylinder Models (Axons, e.g. [Assaf et al. 2004])](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_cylinder_models.ipynb) -- [Sphere Models (Tumor cells, e.g. [Panagiotaki et al. 2014])](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_sphere_models.ipynb) -- [Parameter Distribution Models (Axon Diameter Distribution, e.g. [Assaf et al. 2008])](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_diameter_distributions.ipynb) -- [Gaussian Models (Extra-axonal, e.g. [Behrens et al. 2003])](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_gaussian_models.ipynb) +- [Cylinder Models (Axons, e.g. [Assaf et al. 2004])](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_cylinder_models.ipynb) +- [Sphere Models (Tumor cells, e.g. [Panagiotaki et al. 2014])](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_sphere_models.ipynb) +- [Parameter Distribution Models (Axon Diameter Distribution, e.g. [Assaf et al. 2008])](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_diameter_distributions.ipynb) +- [Gaussian Models (Extra-axonal, e.g. [Behrens et al. 2003])](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_gaussian_models.ipynb) - Tissue Response Function Models and Estimation (WM/GM/CSF, e.g. [Jeurissen et al. 2014]) -- [Spherical Distribution Models (Axon Dispersion, e.g. [Kaden et al. 2007])](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_watson_bingham.ipynb) -- [Spherical Mean of any Compartment Model](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_spherical_mean_models.ipynb) +- [Spherical Distribution Models (Axon Dispersion, e.g. [Kaden et al. 2007])](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_watson_bingham.ipynb) +- [Spherical Mean of any Compartment Model](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_spherical_mean_models.ipynb) ### Global Multi-Compartment Optimizers - [Brute Force to Gradient Descent (Brute2Fine)](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_brute_force_optimization.ipynb) - [Stochastic (MIX) [Farooq et al. 2016]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_stochastic_mix_optimization.ipynb) ### Constrained Spherical Deconvolution Optimizers - [Generalized Multi-Shell Multi-Compartment CSD [Tournier et al. 2007, Jeurissen et al. 2014]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_generalized_csd_optimizer.ipynb) ## Dmipy implementations of Microstructure Models in Literature -Dmipy uses HCP data to illustrate microstructure model examples. To reproduce these examples, dmipy provides a direct way to download HCP data (using your own AWS credentials) in the [HCP tutorial](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/tutorial_human_connectome_project_aws.ipynb). +Dmipy uses HCP data to illustrate microstructure model examples. To reproduce these examples, dmipy provides a direct way to download HCP data (using your own AWS credentials) in the [HCP tutorial](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/tutorial_human_connectome_project_aws.ipynb). ### Single Bundle Models -- [Ball and Stick [Behrens et al. 2003]](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_ball_and_stick.ipynb) -- [Ball and Racket [Sotiropoulos et al. 2012]](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_ball_and_racket.ipynb) -- [NODDI-Watson [Zhang et al. 2012]](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_noddi_watson.ipynb) -- [NODDI-Bingham [Tariq et al. 2016]](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_noddi_bingham.ipynb) -- [AxCaliber [Assaf et al. 2008]](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_axcaliber.ipynb) +- [Ball and Stick [Behrens et al. 2003]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_ball_and_stick.ipynb) +- [Ball and Racket [Sotiropoulos et al. 2012]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_ball_and_racket.ipynb) +- [NODDI-Watson [Zhang et al. 2012]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_noddi_watson.ipynb) +- [NODDI-Bingham [Tariq et al. 2016]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_noddi_bingham.ipynb) +- [AxCaliber [Assaf et al. 2008]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_axcaliber.ipynb) - [AxCaliber with Extra-Axonal Diffusion Time-Dependence [De Santis et al. 2016]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_axcaliber_temporal_zeppeline.ipynb) ### Crossing Bundle Models -- [Ball and Sticks [Behrens et al. 2003]](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_ball_and_sticks.ipynb) -- [NODDI in Crossings (NODDIx) [Farooq et al. 2016]](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_mix_microstructure_imaging_in_crossings.ipynb) +- [Ball and Sticks [Behrens et al. 2003]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_ball_and_sticks.ipynb) +- [NODDI in Crossings (NODDIx) [Farooq et al. 2016]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_mix_microstructure_imaging_in_crossings.ipynb) ### Tumor Models -- [VERDICT [Panagiotaki et al. 2014]](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_verdict.ipynb) +- [IVIM [Le Bihan et al. 1988, Gurney-Champion et al. 2018]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_ivim.ipynb) +- [VERDICT [Panagiotaki et al. 2014]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_verdict.ipynb) ### Spherical Mean-Based Models Any Spherical Mean model can also estimate parametric FODs. - [Spherical Mean Technique [Kaden et al. 2015]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_spherical_mean_technique.ipynb) -- [Multi-Compartment Microscopic Diffusion Imaging [Kaden et al. 2016]](http://nbviewer.jupyter.org/github/AthenaEPI/mipy/blob/master/examples/example_multi_compartment_spherical_mean_technique.ipynb) +- [Multi-Compartment Microscopic Diffusion Imaging [Kaden et al. 2016]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_multi_compartment_spherical_mean_technique.ipynb) ### Spherical Deconvolution-Based Models Constrained spherical deconvolution (CSD) models are primarily used for Fiber Orientation Distribution (FOD) estimation. Multi-Tissue CSD models improve FOD estimation by separating WM/GM/CSF signal contributions using multiple tissue response functions. - [Multi-Shell Multi-Compartment CSD [model-based version of Jeurissen et al. 2014]](http://nbviewer.jupyter.org/github/AthenaEPI/dmipy/blob/master/examples/example_multi_compartment_constrained_spherical_deconvolution.ipynb) diff --git a/dmipy/core/acquisition_scheme.py b/dmipy/core/acquisition_scheme.py index 954c40f4..d300988c 100644 --- a/dmipy/core/acquisition_scheme.py +++ b/dmipy/core/acquisition_scheme.py @@ -165,9 +165,11 @@ def __init__(self, bvalues, gradient_directions, qvalues, self.shell_sh_orders[shell_index], theta_, phi_)[0] # warning in case there are no b0 measurements if sum(self.b0_mask) == 0: - msg = "No b0 measurements were detected. Check if the b0_threshold" - msg += " option is high enough, or if there is a mistake in the " - msg += "acquisition design." + msg = "No b0 measurements were detected. If this is the case, you " + msg += "need to estimate it from the data by setting " + msg += "optimize_S0=True when fitting. If not, check if the " + msg += "b0_threshold option is high enough, or if there is a " + msg += "mistake in the acquisition design." warn(msg) self.spherical_mean_scheme = SphericalMeanAcquisitionScheme( @@ -362,8 +364,12 @@ def return_pruned_acquisition_scheme(self, shell_indices, data=None): bvals = self.bvalues[mask] gradient_directions = self.gradient_directions[mask] - delta = self.delta[mask] - Delta = self.Delta[mask] + delta = None + Delta = None + if self.delta is not None: + delta = self.delta[mask] + if self.Delta is not None: + Delta = self.Delta[mask] if self.TE is not None: TE = self.TE[mask] else: diff --git a/dmipy/core/fitted_modeling_framework.py b/dmipy/core/fitted_modeling_framework.py index 17437fbf..d1c6392b 100644 --- a/dmipy/core/fitted_modeling_framework.py +++ b/dmipy/core/fitted_modeling_framework.py @@ -35,11 +35,11 @@ class FittedMultiCompartmentModel: fitted model parameters array. """ - def __init__(self, model, S0, mask, fitted_parameters_vector): + def __init__(self, model, mask, fitted_parameters_vector): self.model = model - self.S0 = S0 self.mask = mask self.fitted_parameters_vector = fitted_parameters_vector + self.S0 = self.fitted_and_linked_parameters['S0'] @property def fitted_parameters(self): @@ -179,9 +179,9 @@ def predict(self, acquisition_scheme=None, S0=None, mask=None): dataset_shape = self.fitted_parameters_vector.shape[:-1] if S0 is None: - S0 = self.S0 + S0_mult = np.ones_like(self.S0) elif isinstance(S0, float): - S0 = np.ones(dataset_shape) * S0 + S0_mult = S0 / self.S0 if mask is None: mask = self.mask @@ -193,7 +193,8 @@ def predict(self, acquisition_scheme=None, S0=None, mask=None): parameters = self.model.parameter_vector_to_parameters( self.fitted_parameters_vector[pos]) predicted_signal[pos] = self.model( - acquisition_scheme, **parameters) * S0[pos] + acquisition_scheme, **parameters) * np.dot( + self.model.S0_mapping, S0_mult[pos]) return predicted_signal def R2_coefficient_of_determination(self, data): @@ -241,11 +242,11 @@ class FittedMultiCompartmentSphericalMeanModel: fitted model parameters array. """ - def __init__(self, model, S0, mask, fitted_parameters_vector): + def __init__(self, model, mask, fitted_parameters_vector): self.model = model - self.S0 = S0 self.mask = mask self.fitted_parameters_vector = fitted_parameters_vector + self.S0 = self.fitted_and_linked_parameters['S0'] @property def fitted_parameters(self): @@ -292,9 +293,9 @@ def predict(self, acquisition_scheme=None, S0=None, mask=None): dataset_shape = self.fitted_parameters_vector.shape[:-1] if S0 is None: - S0 = self.S0 + S0_mult = np.ones_like(self.S0) elif isinstance(S0, float): - S0 = np.ones(dataset_shape) * S0 + S0_mult = S0 / self.S0 if mask is None: mask = self.mask @@ -306,7 +307,8 @@ def predict(self, acquisition_scheme=None, S0=None, mask=None): parameters = self.model.parameter_vector_to_parameters( self.fitted_parameters_vector[pos]) predicted_signal[pos] = self.model( - acquisition_scheme, **parameters) * S0[pos] + acquisition_scheme, **parameters) * np.dot( + self.model.S0_mapping_sm, S0_mult[pos]) return predicted_signal def R2_coefficient_of_determination(self, data): @@ -447,6 +449,8 @@ def return_parametric_fod_model( parameters[i]) for parameters in parameter_pairs: + if smt_par_name == 'S0': # temporary solution + continue smt_parameter_name = parameters[0] mc_parameter_name = parameters[1] mc_bundles_model.set_fixed_parameter( @@ -526,6 +530,8 @@ def return_spherical_harmonics_fod_model(self, sh_order=8): del sh_model.parameter_optimization_flags[param_to_delete] for smt_par_name in self.model.parameter_names: + if smt_par_name == 'S0': # temporary solution + continue sh_model.set_fixed_parameter( smt_par_name, self.fitted_parameters[smt_par_name]) return sh_model diff --git a/dmipy/core/modeling_framework.py b/dmipy/core/modeling_framework.py index c0aca6e2..b58bef29 100644 --- a/dmipy/core/modeling_framework.py +++ b/dmipy/core/modeling_framework.py @@ -471,6 +471,7 @@ def set_initial_guess_parameter(self, parameter_name, value): msg = '{} does not exist or has already been fixed.'.format( parameter_name) raise ValueError(msg) + self.parameter_optimization_flags[parameter_name] = True def _add_initial_guess_parameter_array( self, parameter_name, parameter_array): @@ -506,11 +507,14 @@ def set_fixed_parameter(self, parameter_name, value): float(value)) elif isinstance(value, np.ndarray): self._add_fixed_parameter_array(parameter_name, value) - elif card == 2: + else: + msg = 'parameter_name must be a float or an ND-array.' + raise ValueError(msg) + elif card > 1: value = np.array(value, dtype=float) - if value.shape[-1] != 2: - msg = '{} can only be fixed '.format(parameter_name) - msg += 'to an array or list with last dimension 2.' + if value.shape[-1] != card: + msg = '{} can only be fixed to an '.format(parameter_name) + msg += 'array or list with last dimension {}.'.format(card) raise ValueError(msg) if value.ndim == 1: self._add_fixed_parameter_value(parameter_name, value) @@ -837,6 +841,126 @@ def _check_tissue_model_acquisition_scheme(self, acquisition_scheme): msg += "model are not the same." raise ValueError(msg) + def _add_S0_parameter(self, data, mask=None, optimize_S0=False): + """ + Adds S0 to the optimized parameters. + + The number of S0 values is the same as the number of TEs in the + acquisition scheme (or 1 when it is not given). + + When the acquisition scheme has b0-measurements, then the initial value + of the S0 parameters is set to the mean of the b0s for that TE. + + The optimize_S0 option tells the optimization to include S0 in the + optimized parameters or not (i.e. if S0 can change to improve the + signal fit). + + If the acquisition scheme has no b0-measurements, and the data has + only one TE, then the S0 is estimated by fitting a Gaussian to the + non-b0 measurements in the acquisition scheme. However, optimize_S0 + must be set to True in this case. When there are multiple TEs and no + b0-measurement it gives a NotImplementedError. + + Parameters + ---------- + data: ND-array of size (Nx, ..., NDWIs), + the data array that is to be fitted. + mask = ND-array of size (Nx, ...), + mask of voxels inside data that are to be fitted. + optimize_S0: bool, + whether or not the optimize S0 as part of the parameters or leave + it fixed as mean of the b0-measurements. + """ + scheme_has_b0s = np.any(self.scheme.b0_mask) + if scheme_has_b0s: + if self.scheme.TE is None or len(np.unique(self.scheme.TE)) == 1: + N_TE = 1 + self.S0_mapping = self.S0_mapping_sm = 1. + S0 = np.mean(data[..., self.scheme.b0_mask], axis=-1) + else: # if multiple TE are in the data + unique_TEs = np.unique(self.scheme.TE) + N_TE = len(unique_TEs) + S0 = np.ones(np.r_[data.shape[:-1], N_TE]) + self.S0_mapping = np.zeros([data.shape[-1], N_TE]) + self.S0_mapping_sm = np.zeros( + [len(self.scheme.shell_TE), N_TE]) + for i, TE_ in enumerate(unique_TEs): + TE_mask = self.scheme.TE == TE_ + TE_mask_sm = self.scheme.shell_TE == TE_ + TE_b0_mask = np.all([self.scheme.b0_mask, TE_mask], axis=0) + S0[..., i] = np.mean( + data[..., TE_b0_mask], axis=-1)[..., None] + self.S0_mapping[:, i] = TE_mask + self.S0_mapping_sm[:, i] = TE_mask_sm + else: + if optimize_S0: + if (self.scheme.TE is None or + len(np.unique(self.scheme.TE)) == 1): + "fit each voxel using 2nd order polynomial" + N_TE = 1 + self.S0_mapping = self.S0_mapping_sm = 1. + S0 = np.zeros(data.shape[:-1]) + mask_pos = np.where(mask) + for pos in zip(*mask_pos): + _, neg_log_S0 = np.polyfit( + x=self.scheme.bvalues, y=-np.log(data[pos]), deg=1) + S0[pos] = np.exp(-neg_log_S0) + else: + raise NotImplementedError( + 'multi-TE S0 estimation not yet implemented') + else: + msg = "optimize_S0 must be True when the acquisition scheme " + msg += "has no b0 measurements." + raise ValueError(msg) + + if N_TE == 1: + parameter_scale = np.max(S0) + S0_norm = S0 / parameter_scale + parameter_range = [0., np.max(S0_norm) * 1.1] + parameter_card = N_TE + parameter_flag = optimize_S0 + else: + parameter_scale = [] + parameter_range = [] + parameter_card = N_TE + parameter_flag = optimize_S0 + for i in range(N_TE): + parameter_scale.append(np.max(S0[..., i])) + S0_norm = S0[..., i] / parameter_scale[i] + parameter_range.append([0., np.max(S0_norm) * 1.1]) + + self._add_optimization_parameter( + 'S0', + parameter_range, + parameter_scale, + parameter_card, + 'intensity', + parameter_flag) + + if optimize_S0: + self.set_initial_guess_parameter('S0', S0) + else: + self.set_fixed_parameter('S0', S0) + + def set_parameter_optimization_bounds(self, parameter_name, bounds): + """ + Sets the parameter optimization bounds for a given parameter. + + Parameters + ---------- + parameter_name: string, + name of the parameter whose bounds should be changed. + bounds: array or size(card, 2), + upper and lower bound for each optimized value for the given + parameter, where card is + self.parameter_cardinality[parameter_name]). + + """ + parameter_scale = np.max(bounds) + ranges = np.array(bounds) / parameter_scale + self.parameter_ranges[parameter_name] = ranges + self.parameter_scales[parameter_name] = parameter_scale + class MultiCompartmentModel(MultiCompartmentModelProperties): r''' @@ -885,10 +1009,10 @@ def _check_for_NMR_and_other_models(self): msg += " into a MultiCompartmentModel." raise ValueError(msg) - def fit(self, acquisition_scheme, data, + def fit(self, acquisition_scheme, data, optimize_S0=False, mask=None, solver='brute2fine', Ns=5, maxiter=300, N_sphere_samples=30, use_parallel_processing=have_pathos, - number_of_processors=None): + number_of_processors=None, verbose=True): """ The main data fitting function of a MultiCompartmentModel. This function can fit it to an N-dimensional dMRI data set, and returns @@ -967,24 +1091,14 @@ def fit(self, acquisition_scheme, data, """ self._check_tissue_model_acquisition_scheme(acquisition_scheme) self._check_model_params_with_acquisition_params(acquisition_scheme) - - # estimate S0 self.scheme = acquisition_scheme - data_ = np.atleast_2d(data) - if self.scheme.TE is None or len(np.unique(self.scheme.TE)) == 1: - S0 = np.mean(data_[..., self.scheme.b0_mask], axis=-1) - else: # if multiple TE are in the data - S0 = np.ones_like(data_) - for TE_ in self.scheme.shell_TE: - TE_mask = self.scheme.TE == TE_ - TE_b0_mask = np.all([self.scheme.b0_mask, TE_mask], axis=0) - S0[..., TE_mask] = np.mean( - data_[..., TE_b0_mask], axis=-1)[..., None] - + data_ = np.atleast_2d(data,) if mask is None: mask = data_[..., 0] > 0 else: mask = np.all([mask, data_[..., 0] > 0], axis=0) + self._add_S0_parameter(data_, mask, optimize_S0) + mask_pos = np.where(mask) N_parameters = len(self.bounds_for_optimization) @@ -995,9 +1109,6 @@ def fit(self, acquisition_scheme, data, **self.x0_parameters) x0_ = homogenize_x0_to_data( data_, x0_) - x0_bool = np.all( - np.isnan(x0_), axis=tuple(np.arange(x0_.ndim - 1))) - x0_[..., ~x0_bool] /= self.scales_for_optimization[~x0_bool] if use_parallel_processing and not have_pathos: msg = 'Cannot use parallel processing without pathos.' @@ -1007,8 +1118,9 @@ def fit(self, acquisition_scheme, data, if number_of_processors is None: number_of_processors = cpu_count() pool = pp.ProcessPool(number_of_processors) - print('Using parallel processing with {} workers.'.format( - number_of_processors)) + if verbose: + print('Using parallel processing with {} workers.'.format( + number_of_processors)) else: fitted_parameters_lin = np.empty( np.r_[N_voxels, N_parameters], dtype=float) @@ -1018,13 +1130,15 @@ def fit(self, acquisition_scheme, data, global_brute = GlobalBruteOptimizer( self, self.scheme, x0_, Ns, N_sphere_samples) fit_func = Brute2FineOptimizer(self, self.scheme, Ns) - print('Setup brute2fine optimizer in {} seconds'.format( - time() - start)) + if verbose: + print('Setup brute2fine optimizer in {} seconds'.format( + time() - start)) elif solver == 'mix': self._check_for_tortuosity_constraint() fit_func = MixOptimizer(self, self.scheme, maxiter) - print('Setup MIX optimizer in {} seconds'.format( - time() - start)) + if verbose: + print('Setup MIX optimizer in {} seconds'.format( + time() - start)) else: msg = "Unknown solver name {}".format(solver) raise ValueError(msg) @@ -1032,13 +1146,12 @@ def fit(self, acquisition_scheme, data, start = time() for idx, pos in enumerate(zip(*mask_pos)): - voxel_E = data_[pos] / S0[pos] + voxel_E = data_[pos] # / S0[pos] voxel_x0_vector = x0_[pos] if solver == 'brute2fine': if global_brute.global_optimization_grid is True: - voxel_x0_vector = global_brute(voxel_E) + voxel_x0_vector = global_brute(voxel_E, voxel_x0_vector[0]) fit_args = (voxel_E, voxel_x0_vector) - if use_parallel_processing: fitted_parameters_lin[idx] = pool.apipe(fit_func, *fit_args) else: @@ -1048,17 +1161,18 @@ def fit(self, acquisition_scheme, data, [p.get() for p in fitted_parameters_lin]) fitting_time = time() - start - print('Fitting of {} voxels complete in {} seconds.'.format( - len(fitted_parameters_lin), fitting_time)) - print('Average of {} seconds per voxel.'.format( - fitting_time / N_voxels)) + if verbose: + print('Fitting of {} voxels complete in {} seconds.'.format( + len(fitted_parameters_lin), fitting_time)) + print('Average of {} seconds per voxel.'.format( + fitting_time / N_voxels)) fitted_parameters = np.zeros_like(x0_, dtype=float) fitted_parameters[mask_pos] = ( fitted_parameters_lin * self.scales_for_optimization) return FittedMultiCompartmentModel( - self, S0, mask, fitted_parameters) + self, mask, fitted_parameters) def simulate_signal(self, acquisition_scheme, parameters_array_or_dict): """ @@ -1129,6 +1243,12 @@ def __call__(self, acquisition_scheme_or_vertices, kwargs: keyword arguments to the model parameter values, Is internally given as **parameter_dictionary. """ + try: + S0_pars = kwargs['S0'] + S0 = np.dot(self.S0_mapping, S0_pars) + except KeyError: + S0 = 1. + if quantity == "signal" or quantity == "FOD": values = 0 elif quantity == "stochastic cost function": @@ -1162,11 +1282,8 @@ def __call__(self, acquisition_scheme_or_vertices, ) if quantity == "signal": - values = ( - values + - partial_volume * model( - acquisition_scheme_or_vertices, **parameters) - ) + values += S0 * partial_volume * model( + acquisition_scheme_or_vertices, **parameters) elif quantity == "FOD": try: values = ( @@ -1177,8 +1294,8 @@ def __call__(self, acquisition_scheme_or_vertices, except AttributeError: continue elif quantity == "stochastic cost function": - values[:, counter] = model(acquisition_scheme_or_vertices, - **parameters) + values[:, counter] = S0 * model(acquisition_scheme_or_vertices, + **parameters) counter += 1 return values @@ -1250,10 +1367,10 @@ def _delete_orientation_parameters(self): del self.parameter_cardinality[appended_param_name] del self.parameter_types[appended_param_name] - def fit(self, acquisition_scheme, data, + def fit(self, acquisition_scheme, data, optimize_S0=False, mask=None, solver='brute2fine', Ns=5, maxiter=300, N_sphere_samples=30, use_parallel_processing=have_pathos, - number_of_processors=None): + number_of_processors=None, verbose=True): """ The main data fitting function of a MultiCompartmentModel. This function can fit it to an N-dimensional dMRI data set, and returns @@ -1332,27 +1449,15 @@ def fit(self, acquisition_scheme, data, """ self._check_tissue_model_acquisition_scheme(acquisition_scheme) self._check_model_params_with_acquisition_params(acquisition_scheme) - - # estimate S0 self.scheme = acquisition_scheme data_ = np.atleast_2d(data) - if self.scheme.TE is None or len(np.unique(self.scheme.TE)) == 1: - S0 = np.mean(data_[..., self.scheme.b0_mask], axis=-1) - else: # if multiple TE are in the data - S0 = np.ones(np.r_[data_.shape[:-1], - len(acquisition_scheme.shell_TE)]) - for TE_ in self.scheme.shell_TE: - TE_mask = self.scheme.shell_TE == TE_ - TE_mask_shell = self.scheme.TE == TE_ - TE_b0_mask = np.all([self.scheme.b0_mask, TE_mask_shell], - axis=0) - S0[..., TE_mask] = np.mean( - data_[..., TE_b0_mask], axis=-1)[..., None] if mask is None: mask = data_[..., 0] > 0 else: mask = np.all([mask, data_[..., 0] > 0], axis=0) + self._add_S0_parameter(data_, mask, optimize_S0) + mask_pos = np.where(mask) N_parameters = len(self.bounds_for_optimization) @@ -1364,9 +1469,6 @@ def fit(self, acquisition_scheme, data, **self.x0_parameters) x0_ = homogenize_x0_to_data( data_, x0_) - x0_bool = np.all( - np.isnan(x0_), axis=tuple(np.arange(x0_.ndim - 1))) - x0_[..., ~x0_bool] /= self.scales_for_optimization[~x0_bool] if use_parallel_processing and not have_pathos: msg = 'Cannot use parallel processing without pathos.' @@ -1376,8 +1478,9 @@ def fit(self, acquisition_scheme, data, if number_of_processors is None: number_of_processors = cpu_count() pool = pp.ProcessPool(number_of_processors) - print('Using parallel processing with {} workers.'.format( - number_of_processors)) + if verbose: + print('Using parallel processing with {} workers.'.format( + number_of_processors)) else: fitted_parameters_lin = np.empty( np.r_[N_voxels, N_parameters], dtype=float) @@ -1396,13 +1499,15 @@ def fit(self, acquisition_scheme, data, self, self.scheme, x0_, Ns, N_sphere_samples) fit_func = Brute2FineOptimizer(self, self.scheme, Ns) - print('Setup brute2fine optimizer in {} seconds'.format( - time() - start)) + if verbose: + print('Setup brute2fine optimizer in {} seconds'.format( + time() - start)) elif solver == 'mix': self._check_for_tortuosity_constraint() fit_func = MixOptimizer(self, self.scheme, maxiter) - print('Setup MIX optimizer in {} seconds'.format( - time() - start)) + if verbose: + print('Setup MIX optimizer in {} seconds'.format( + time() - start)) else: msg = "Unknown solver name {}".format(solver) raise ValueError(msg) @@ -1410,11 +1515,11 @@ def fit(self, acquisition_scheme, data, start = time() for idx, pos in enumerate(zip(*mask_pos)): - voxel_E = data_to_fit[pos] / S0[pos] + voxel_E = data_to_fit[pos] voxel_x0_vector = x0_[pos] if solver == 'brute2fine': if global_brute.global_optimization_grid is True: - voxel_x0_vector = global_brute(voxel_E) + voxel_x0_vector = global_brute(voxel_E, voxel_x0_vector[0]) fit_args = (voxel_E, voxel_x0_vector) if use_parallel_processing: @@ -1426,17 +1531,18 @@ def fit(self, acquisition_scheme, data, [p.get() for p in fitted_parameters_lin]) fitting_time = time() - start - print('Fitting of {} voxels complete in {} seconds.'.format( - len(fitted_parameters_lin), fitting_time)) - print('Average of {} seconds per voxel.'.format( - fitting_time / N_voxels)) + if verbose: + print('Fitting of {} voxels complete in {} seconds.'.format( + len(fitted_parameters_lin), fitting_time)) + print('Average of {} seconds per voxel.'.format( + fitting_time / N_voxels)) fitted_parameters = np.zeros_like(x0_, dtype=float) fitted_parameters[mask_pos] = ( fitted_parameters_lin * self.scales_for_optimization) return FittedMultiCompartmentSphericalMeanModel( - self, S0, mask, fitted_parameters) + self, mask, fitted_parameters) def simulate_signal(self, acquisition_scheme, parameters_array_or_dict): """ @@ -1507,6 +1613,12 @@ def __call__(self, acquisition_scheme_or_vertices, kwargs: keyword arguments to the model parameter values, Is internally given as **parameter_dictionary. """ + try: + S0_pars = kwargs['S0'] + S0 = np.dot(self.S0_mapping_sm, S0_pars) + except KeyError: + S0 = 1. + if quantity == "signal": values = 0 elif quantity == "stochastic cost function": @@ -1540,13 +1652,10 @@ def __call__(self, acquisition_scheme_or_vertices, ) if quantity == "signal": - values = ( - values + - partial_volume * model.spherical_mean( - acquisition_scheme_or_vertices, **parameters) - ) + values += S0 * partial_volume * model.spherical_mean( + acquisition_scheme_or_vertices, **parameters) elif quantity == "stochastic cost function": - values[:, counter] = model.spherical_mean( + values[:, counter] = S0 * model.spherical_mean( acquisition_scheme_or_vertices, **parameters) counter += 1 diff --git a/dmipy/core/tests/test_optimization.py b/dmipy/core/tests/test_optimization.py index 9f6f4dbc..b493ccb3 100644 --- a/dmipy/core/tests/test_optimization.py +++ b/dmipy/core/tests/test_optimization.py @@ -1,8 +1,7 @@ from dmipy.signal_models import ( cylinder_models, gaussian_models, sphere_models) from dmipy.core import modeling_framework -from numpy.testing import ( - assert_equal, assert_array_almost_equal, assert_array_equal) +from numpy.testing import assert_array_almost_equal import numpy as np from dmipy.data.saved_acquisition_schemes import wu_minn_hcp_acquisition_scheme @@ -17,16 +16,23 @@ def test_simple_stick_optimization(): stick_model = modeling_framework.MultiCompartmentModel( models=[stick]) + gt_parameters = {'C1Stick_1_lambda_par': gt_lambda_par, + 'C1Stick_1_mu': gt_mu} + gt_parameter_vector = stick_model.parameters_to_parameter_vector( - C1Stick_1_lambda_par=gt_lambda_par, C1Stick_1_mu=gt_mu) + **gt_parameters) E = stick_model.simulate_signal(scheme, gt_parameter_vector) stick_model.set_initial_guess_parameter('C1Stick_1_lambda_par', (np.random.rand() + 1.) * 1e-9) stick_model.set_initial_guess_parameter('C1Stick_1_mu', np.random.rand(2)) - res = stick_model.fit(scheme, E).fitted_parameters_vector - assert_array_almost_equal(gt_parameter_vector, res.squeeze(), 2) + fit = stick_model.fit(scheme, E) + for parname, gt_value in gt_parameters.items(): + fitval = fit.fitted_parameters[parname][0] + scale = stick_model.parameter_scales[parname] + assert_array_almost_equal( + abs(fitval / scale), gt_value / scale, 2) def test_simple_ball_and_stick_optimization(): @@ -42,13 +48,14 @@ def test_simple_ball_and_stick_optimization(): gt_lambda_iso = gt_lambda_par / 2. gt_partial_volume = 0.3 + gt_parameters = {'C1Stick_1_lambda_par': gt_lambda_par, + 'G1Ball_1_lambda_iso': gt_lambda_iso, + 'C1Stick_1_mu': gt_mu, + 'partial_volume_0': gt_partial_volume, + 'partial_volume_1': 1 - gt_partial_volume} + gt_parameter_vector = ball_and_stick.parameters_to_parameter_vector( - C1Stick_1_lambda_par=gt_lambda_par, - G1Ball_1_lambda_iso=gt_lambda_iso, - C1Stick_1_mu=gt_mu, - partial_volume_0=gt_partial_volume, - partial_volume_1=1 - gt_partial_volume - ) + **gt_parameters) E = ball_and_stick.simulate_signal( scheme, gt_parameter_vector) @@ -63,8 +70,12 @@ def test_simple_ball_and_stick_optimization(): ball_and_stick.set_initial_guess_parameter('partial_volume_0', vf_rand) ball_and_stick.set_initial_guess_parameter('partial_volume_1', 1 - vf_rand) - res = ball_and_stick.fit(scheme, E).fitted_parameters_vector - assert_array_almost_equal(gt_parameter_vector, res.squeeze(), 2) + fit = ball_and_stick.fit(scheme, E) + for parname, gt_value in gt_parameters.items(): + fitval = fit.fitted_parameters[parname][0] + scale = ball_and_stick.parameter_scales[parname] + assert_array_almost_equal( + abs(fitval / scale), gt_value / scale, 2) def test_multi_dimensional_x0(): @@ -84,14 +95,15 @@ def test_multi_dimensional_x0(): for j, mu2 in enumerate(np.linspace(-np.pi, np.pi, 10)): gt_mu_array[i, j] = np.r_[mu1, mu2] + gt_parameters = {'C1Stick_1_lambda_par': gt_lambda_par, + 'G1Ball_1_lambda_iso': gt_lambda_iso, + 'C1Stick_1_mu': gt_mu_array, + 'partial_volume_0': gt_partial_volume, + 'partial_volume_1': 1 - gt_partial_volume} + gt_parameter_vector = ( ball_and_stick.parameters_to_parameter_vector( - C1Stick_1_lambda_par=gt_lambda_par, - G1Ball_1_lambda_iso=gt_lambda_iso, - C1Stick_1_mu=gt_mu_array, - partial_volume_0=gt_partial_volume, - partial_volume_1=1 - gt_partial_volume) - ) + **gt_parameters)) E_array = ball_and_stick.simulate_signal( scheme, gt_parameter_vector) @@ -107,16 +119,12 @@ def test_multi_dimensional_x0(): ball_and_stick.set_initial_guess_parameter( 'partial_volume_1', 1 - gt_partial_volume) # I'm giving a voxel-dependent initial condition with gt_mu_array - res = ball_and_stick.fit(scheme, E_array).fitted_parameters_vector - # optimization should stop immediately as I'm giving the ground truth. - assert_equal(np.all(np.ravel(res - gt_parameter_vector) == 0.), True) + fit = ball_and_stick.fit(scheme, E_array) # and the parameter vector dictionaries of the results and x0 should also # be the same. - res_parameters = ball_and_stick.parameter_vector_to_parameters(res) - x0_parameters = ball_and_stick.parameter_vector_to_parameters( - gt_parameter_vector) - for key in res_parameters.keys(): - assert_array_equal(x0_parameters[key], res_parameters[key]) + for parname, gt_value in gt_parameters.items(): + fitval = fit.fitted_parameters[parname] + assert_array_almost_equal(gt_parameters[parname], fitval) def test_stick_and_tortuous_zeppelin_to_spherical_mean_fit(): @@ -156,13 +164,14 @@ def test_stick_and_tortuous_zeppelin_to_spherical_mean_fit(): 'G2Zeppelin_1_lambda_par' ) + gt_parameters = {'C1Stick_1_lambda_par': gt_lambda_par, + 'C1Stick_1_mu': gt_mu, + 'partial_volume_0': gt_partial_volume, + 'partial_volume_1': 1 - gt_partial_volume} + gt_parameter_vector = ( stick_and_zeppelin.parameters_to_parameter_vector( - C1Stick_1_lambda_par=gt_lambda_par, - C1Stick_1_mu=gt_mu, - partial_volume_0=gt_partial_volume, - partial_volume_1=1 - gt_partial_volume) - ) + **gt_parameters)) E = stick_and_zeppelin.simulate_signal( scheme, gt_parameter_vector) @@ -183,11 +192,14 @@ def test_stick_and_tortuous_zeppelin_to_spherical_mean_fit(): 'G2Zeppelin_1_lambda_par', 'C1Stick_1_lambda_par') - res_sm = stick_and_tortuous_zeppelin_sm.fit(scheme, E - ).fitted_parameters_vector - - assert_array_almost_equal( - np.r_[gt_lambda_par, gt_partial_volume], res_sm.squeeze()[:-1], 2) + fit = stick_and_tortuous_zeppelin_sm.fit(scheme, E) + for parname, gt_value in gt_parameters.items(): + if parname not in fit.fitted_parameters.keys(): + continue + fitval = fit.fitted_parameters[parname][0] + scale = stick_and_tortuous_zeppelin_sm.parameter_scales[parname] + assert_array_almost_equal( + abs(fitval / scale), gt_value / scale, 2) def test_fractions_add_up_to_one(): @@ -217,21 +229,26 @@ def test_MIX_fitting_multimodel(): modeling_framework.MultiCompartmentModel( models=[ball, zeppelin])) + gt_parameters = {'G1Ball_1_lambda_iso': 2.7e-9, + 'partial_volume_0': .2, + 'partial_volume_1': .8, + 'G2Zeppelin_1_lambda_perp': .5e-9, + 'G2Zeppelin_1_mu': (np.pi / 2., np.pi / 2.), + 'G2Zeppelin_1_lambda_par': 1.7e-9} + parameter_vector = ball_and_zeppelin.parameters_to_parameter_vector( - G1Ball_1_lambda_iso=2.7e-9, - partial_volume_0=.2, - partial_volume_1=.8, - G2Zeppelin_1_lambda_perp=.5e-9, - G2Zeppelin_1_mu=(np.pi / 2., np.pi / 2.), - G2Zeppelin_1_lambda_par=1.7e-9 - ) + **gt_parameters) E = ball_and_zeppelin.simulate_signal( scheme, parameter_vector) fit = ball_and_zeppelin.fit( scheme, - E, solver='mix').fitted_parameters_vector - assert_array_almost_equal(abs(fit).squeeze(), parameter_vector, 2) + E, solver='mix') + for parname, gt_value in gt_parameters.items(): + fitval = fit.fitted_parameters[parname][0] + scale = ball_and_zeppelin.parameter_scales[parname] + assert_array_almost_equal( + abs(fitval / scale), gt_value / scale, 2) def test_MIX_fitting_singlemodel(): @@ -240,14 +257,19 @@ def test_MIX_fitting_singlemodel(): modeling_framework.MultiCompartmentModel( models=[stick])) + gt_parameters = {'C1Stick_1_mu': [np.pi / 2., np.pi / 2.], + 'C1Stick_1_lambda_par': 1.7e-9} + parameter_vector = stick_mod.parameters_to_parameter_vector( - C1Stick_1_mu=(np.pi / 2., np.pi / 2.), - C1Stick_1_lambda_par=1.7e-9 - ) + **gt_parameters) E = stick_mod.simulate_signal( scheme, parameter_vector) fit = stick_mod.fit( scheme, - E, solver='mix').fitted_parameters_vector - assert_array_almost_equal(abs(fit).squeeze(), parameter_vector, 2) + E, solver='mix') + for parname, gt_value in gt_parameters.items(): + fitval = fit.fitted_parameters[parname][0] + scale = stick_mod.parameter_scales[parname] + assert_array_almost_equal( + abs(fitval / scale), gt_value / scale, 2) diff --git a/dmipy/custom_optimizers/intra_voxel_incoherent_motion.py b/dmipy/custom_optimizers/intra_voxel_incoherent_motion.py new file mode 100644 index 00000000..baf62366 --- /dev/null +++ b/dmipy/custom_optimizers/intra_voxel_incoherent_motion.py @@ -0,0 +1,204 @@ +from dmipy.signal_models.gaussian_models import G1Ball +from dmipy.core.modeling_framework import MultiCompartmentModel +from dmipy.core.acquisition_scheme import acquisition_scheme_from_bvalues +import numpy as np +from time import time + + +def ivim_2step(acquisition_scheme, data, mask=None, bvalue_threshold=4e8, + solver='brute2fine', optimize_S0=True, **fit_args): + """ + Dmipy implementation of the classic 2-compartment intra-voxel incoherent + motion (IVIM) model [1]_, following the 2-step optimization scheme. The + model consists of 2 Ball compartments (isotropic Gaussian), each fitting + the blood flow and diffusion volume fractions and diffusivities, + respectively. Changes in e.g. blood volume fraction has been linked to many + pathologies such as the vasculature in tumor tissue [2]_. + + Because the apparent diffusivity of blood flow is much higher than that of + Brownian motion, the optimization bounds for the diffusivities of the two + Balls are disjoint; the diffusivies of the diffusion compartment range + between [0.5 - 6]e-3 mm^2/s (results in more precise fit according to [3]), + and those of the blood compartment range between [6 - 20]e-3 mm^2/s + (following [4]). + + The 2-step optimization [5] hinges on the observation that the blood-flow + signal is negligible at b-values above 200-400 s/mm^2, but it does have + a constribution below that bvalue (and to the b0). + The optimization steps are as follows: + - step 1: fit only the "diffusion" part of the data using a single Ball + compartment, so the data is truncated to only include measurements + above the bvalue_threshold value. This step estimates the "diffusion" + S0 (which is lower or equal to the actual SO) and the "diffusion" + diffusivity of this compartment. + - step 2: fit the 2-compartment model to the whole signal, but fixing the + "diffusion" diffusivity to the value estimated in step 1. + + In the fitted ivim_fit model, partial_volume_0 and G1Ball_1_lambda_iso + represent the tissue fraction and diffusivity, and partial_volume_1 and + G1Ball_2_lambda_iso represent the blood fraction and diffusivity. + + Parameters + ---------- + acquisition_scheme: Dmipy AcquisitionScheme instance, + acquisition scheme containing all the information of the ivim + acquisition. + data: ND-array of shape (Nx, ..., N_DWI), + measured data corresponding to the acquisition scheme. + mask : (N-1)-dimensional integer/boolean array of size (N_x, N_y, ...), + Optional mask of voxels to be included in the optimization. + bvalue_threshold: float, + the bvalue threshold at which to separate the blood/diffusion parts of + the data. + Default: 400s/mm^2, but other works experiment with this value. + solver: float, + which solver to use for the algorithm. Default: 'brute2fine'. + optimize_S0: boolean, + whether or not to optimize (or just fix it to the mean of the b0-data) + the S0 value in the second optimization step. + fit_args: other keywords that are passed to the optimizer + + Returns + ------- + ivim_fit: Dmipy FittedMultiCompartmentModel instance, + contains the fitted IVIM parameters. + + References + ---------- + .. [1] Le Bihan, D., Breton, E., Lallemand, D., Aubin, M. L., Vignaud, J., + & Laval-Jeantet, M. (1988). Separation of diffusion and perfusion in + intravoxel incoherent motion MR imaging. Radiology, 168(2), 497-505. + .. [2] Le Bihan, D. (2017). What can we see with IVIM MRI?. NeuroImage. + .. [3] Gurney-Champion OJ, Froeling M, Klaassen R, Runge JH, Bel A, Van + Laarhoven HWM, et al. Minimizing the Acquisition Time for Intravoxel + Incoherent Motion Magnetic Resonance Imaging Acquisitions in the Liver + and Pancreas. Invest Radiol. 2016;51: 211–220. + .. [4] Park HJ, Sung YS, Lee SS, Lee Y, Cheong H, Kim YJ, et al. Intravoxel + incoherent motion diffusion-weighted MRI of the abdomen: The effect of + fitting algorithms on the accuracy and reliability of the parameters. + J Magn Reson Imaging. 2017;45: 1637–1647. + """ + start = time() + + if fit_args is None: + fit_args = {} + fit_args.update({'verbose': False, 'mask': mask, 'solver': solver}) + + bvalue_mask = acquisition_scheme.bvalues > bvalue_threshold + gaussian_acquisition_scheme = acquisition_scheme_from_bvalues( + bvalues=acquisition_scheme.bvalues[bvalue_mask], + gradient_directions=acquisition_scheme.gradient_directions[ + bvalue_mask]) + + gaussian_data = np.atleast_2d(data)[..., bvalue_mask] + + gaussian_mod = MultiCompartmentModel([G1Ball()]) + gaussian_mod.set_parameter_optimization_bounds( + 'G1Ball_1_lambda_iso', [0.5e-9, 6e-9]) # [3] + print('Starting step 1 of IVIM 2-step algorithm.') + gaussian_fit = gaussian_mod.fit( + acquisition_scheme=gaussian_acquisition_scheme, + data=gaussian_data, + optimize_S0=True, + **fit_args) + + ivim_mod = MultiCompartmentModel([G1Ball(), G1Ball()]) + ivim_mod.set_parameter_optimization_bounds( + 'G1Ball_2_lambda_iso', [6e-9, 20e-9]) # [4] + ivim_mod.set_fixed_parameter( + parameter_name='G1Ball_1_lambda_iso', + value=gaussian_fit.fitted_parameters['G1Ball_1_lambda_iso']) + print('Starting step 2 of IVIM 2-step algorithm.') + ivim_fit = ivim_mod.fit( + acquisition_scheme=acquisition_scheme, + data=data, + optimize_S0=optimize_S0, + **fit_args) + + computation_time = time() - start + N_voxels = np.sum(ivim_fit.mask) + msg = 'IVIM 2-step optimization of {0:d} voxels'.format(N_voxels) + msg += ' complete in {0:.3f} seconds'.format(computation_time) + print(msg) + return ivim_fit + + +def ivim_Dstar_fixed(acquisition_scheme, data, mask=None, Dstar_value=7e-9, + solver='brute2fine', optimize_S0=True, **fit_args): + """ + Implementation of second best performing IVIM algorithm following [1]_. + Basically, it is just a non-linear least squares fit with fixing the + blood diffusivity Dstar to 7e-3 mm^2/s. This value apparently improves the + stability of the fit (in healthy volunteers) [2]_. + + The optimization range for the tissue diffusivity is set to + [0.5 - 6]e-3 mm^2/s to improve precision [3]_. + + In the fitted ivim_fit model, partial_volume_0 and G1Ball_1_lambda_iso + represent the tissue fraction and diffusivity, and partial_volume_1 and + G1Ball_2_lambda_iso represent the blood fraction and diffusivity. + + Parameters + ---------- + acquisition_scheme: Dmipy AcquisitionScheme instance, + acquisition scheme containing all the information of the ivim + acquisition. + data: ND-array of shape (Nx, ..., N_DWI), + measured data corresponding to the acquisition scheme. + mask : (N-1)-dimensional integer/boolean array of size (N_x, N_y, ...), + Optional mask of voxels to be included in the optimization. + Dstar_value: float, + the fixed Dstar blood diffusivity value. Default: 7e-9 m^2/s [2]_. + solver: float, + which solver to use for the algorithm. Default: 'brute2fine'. + optimize_S0: boolean, + whether or not to optimize (or just fix it to the mean of the b0-data) + the S0 value in the second optimization step. + fit_args: other keywords that are passed to the optimizer + + Returns + ------- + ivim_fit: Dmipy FittedMultiCompartmentModel instance, + contains the fitted IVIM parameters. + + References + ---------- + .. [1] Gurney-Champion, O. J., Klaassen, R., Froeling, M., Barbieri, S., + Stoker, J., Engelbrecht, M. R., ... & Nederveen, A. J. (2018). + Comparison of six fit algorithms for the intra-voxel incoherent motion + model of diffusion-weighted magnetic resonance imaging data of + pancreatic cancer patients. PloS one, 13(4), e0194590. + .. [2] Gurney-Champion OJ, Froeling M, Klaassen R, Runge JH, Bel A, Van + Laarhoven HWM, et al. Minimizing the Acquisition Time for Intravoxel + Incoherent Motion Magnetic Resonance Imaging Acquisitions in the Liver + and Pancreas. Invest Radiol. 2016;51: 211–220. + .. [3] Park HJ, Sung YS, Lee SS, Lee Y, Cheong H, Kim YJ, et al. Intravoxel + incoherent motion diffusion-weighted MRI of the abdomen: The effect of + fitting algorithms on the accuracy and reliability of the parameters. + J Magn Reson Imaging. 2017;45: 1637–1647. + """ + start = time() + + if fit_args is None: + fit_args = {} + + print('Starting IVIM Dstar-fixed algorithm.') + ivim_mod = MultiCompartmentModel([G1Ball(), G1Ball()]) + ivim_mod.set_fixed_parameter( + 'G1Ball_2_lambda_iso', Dstar_value) # following [2] + ivim_mod.set_parameter_optimization_bounds( + 'G1Ball_1_lambda_iso', [.5e-9, 6e-9]) # following [3] + ivim_fit = ivim_mod.fit( + acquisition_scheme=acquisition_scheme, + data=data, + mask=mask, + solver=solver, + optimize_S0=optimize_S0, + verbose=False, + **fit_args) + computation_time = time() - start + N_voxels = np.sum(ivim_fit.mask) + msg = 'IVIM Dstar-fixed optimization of {0:d} voxels'.format(N_voxels) + msg += ' complete in {0:.3f} seconds'.format(computation_time) + print(msg) + return ivim_fit diff --git a/dmipy/optimizers/brute2fine.py b/dmipy/optimizers/brute2fine.py index 3c5888e3..6a9c689d 100644 --- a/dmipy/optimizers/brute2fine.py +++ b/dmipy/optimizers/brute2fine.py @@ -58,7 +58,6 @@ def __init__(self, model, acquisition_scheme, x0_vector=None, Ns=5, N_sphere_samples=30): self.model = model self.acquisition_scheme = acquisition_scheme - self.x0_vector = x0_vector self.Ns = Ns if x0_vector is None: @@ -67,10 +66,15 @@ def __init__(self, model, acquisition_scheme, self.precompute_signal_grid(model, x0_vector, Ns, N_sphere_samples) elif x0_vector.squeeze().ndim == 1: self.global_optimization_grid = True + x0_vector_ = x0_vector.copy().squeeze() + x0_vector_[0] = 1. # setting S0 to 1 for precalculation self.precompute_signal_grid( - model, x0_vector.squeeze(), Ns, N_sphere_samples) - elif np.all(np.isnan(x0_vector.reshape([-1, x0_vector.shape[-1]])[0])): + model, x0_vector_, Ns, N_sphere_samples) + elif np.all(np.isnan( + x0_vector[..., 1:].reshape( + [-1, x0_vector[..., 1:].shape[-1]])[0])): x0_vector_ = np.tile(np.nan, len(model.bounds_for_optimization)) + x0_vector_[0] = 1. self.global_optimization_grid = True self.precompute_signal_grid( model, x0_vector_, Ns, N_sphere_samples) @@ -167,7 +171,7 @@ def precompute_signal_grid(self, model, x0_vector, Ns, N_sphere_samples): self.signal_grid = model.simulate_signal( self.acquisition_scheme, self.parameter_grid) - def __call__(self, data, parameter_scale_normalization=True): + def __call__(self, data, S0): """ Calculates the closest parameter combination based on the sum-squared distances between the measured data and the simulated signal grid. @@ -186,10 +190,9 @@ def __call__(self, data, parameter_scale_normalization=True): estimated closest model parameters in the parameter grid. """ if self.global_optimization_grid is True: - argmin = find_minimum_argument(self.signal_grid, data) + argmin = find_minimum_argument(S0 * self.signal_grid, data) parameters_brute = self.parameter_grid[argmin] - if parameter_scale_normalization: - return parameters_brute / self.model.scales_for_optimization + parameters_brute[0] = S0 return parameters_brute else: msg = "Global Parameter Grid could not be set because parameter " @@ -237,8 +240,13 @@ def __init__(self, model, acquisition_scheme, Ns=5): self.acquisition_scheme = acquisition_scheme self.Ns = Ns - def objective_function(self, parameter_vector, data): + def objective_function( + self, parameter_vector_, data, x0_vector, opt_bools): "The objective function for brute-force and gradient-based optimizer." + parameter_vector = np.empty(len(opt_bools)) + parameter_vector[opt_bools] = parameter_vector_ + parameter_vector[~opt_bools] = x0_vector[~opt_bools] + N_fractions = len(self.model.models) if N_fractions > 1: nested_fractions = parameter_vector[-(N_fractions - 1):] @@ -278,9 +286,11 @@ def __call__(self, data, x0_vector): x_fine: array of size (Nparameters,), array of the optimized model parameters. """ + x0_bool = np.isnan(x0_vector) + x0_vector[~x0_bool] /= self.model.scales_for_optimization[~x0_bool] N_fractions = len(self.model.models) - fit_args = (data,) bounds = self.model.bounds_for_optimization + optimization_bools = np.array(self.model.opt_params_for_optimization) bounds_brute = [] bounds_fine = list(bounds) for i, x0_ in enumerate(x0_vector): @@ -290,25 +300,33 @@ def __call__(self, data, x0_vector): (bounds[i][1] - bounds[i][0]) / float(self.Ns))) if not np.isnan(x0_): bounds_brute.append(slice(x0_, x0_ + 1e-2, None)) - if (not np.isnan(x0_) and - self.model.opt_params_for_optimization[i] is False): - bounds_fine[i] = np.r_[x0_, x0_] + if not np.all(optimization_bools): + bounds_fine = [] + for i, x0_ in enumerate(x0_vector): + if optimization_bools[i]: + bounds_fine.append(bounds[i]) if N_fractions > 1: # go to nested bounds bounds_brute = bounds_brute[:-1] bounds_fine = bounds_fine[:-1] x0_vector = x0_vector[:-1] + optimization_bools = optimization_bools[:-1] + + fit_args_brute = (data, x0_vector, ~np.isnan(x0_vector)) + fit_args_fine = (data, x0_vector, optimization_bools) if np.any(np.isnan(x0_vector)): x0_brute = brute( - self.objective_function, ranges=bounds_brute, args=fit_args, - finish=None) + self.objective_function, ranges=bounds_brute, + args=fit_args_brute, finish=None) else: x0_brute = x0_vector - - x_fine_nested = minimize(self.objective_function, x0_brute, - args=fit_args, bounds=bounds_fine, + x_fine_nested = minimize(self.objective_function, + x0_brute[optimization_bools], + args=fit_args_fine, bounds=bounds_fine, method='L-BFGS-B').x + x0_vector[optimization_bools] = x_fine_nested + x_fine_nested = x0_vector if N_fractions > 1: nested_fractions = x_fine_nested[-(N_fractions - 1):] normalized_fractions = nested_to_normalized_fractions( diff --git a/dmipy/optimizers/mix.py b/dmipy/optimizers/mix.py index c66e36e7..81dd6177 100644 --- a/dmipy/optimizers/mix.py +++ b/dmipy/optimizers/mix.py @@ -76,6 +76,9 @@ def __call__(self, data, x0_vector=np.array([np.nan])): """ # if there is only one model then MIX only uses DE. + x0_bool = np.isnan(x0_vector) + x0_vector[~x0_bool] /= self.model.scales_for_optimization[~x0_bool] + bounds = list(self.model.bounds_for_optimization) if self.Nmodels == 1: bounds_de = bounds diff --git a/dmipy/optimizers_fod/csd_tournier.py b/dmipy/optimizers_fod/csd_tournier.py index f991a191..c4524b4f 100644 --- a/dmipy/optimizers_fod/csd_tournier.py +++ b/dmipy/optimizers_fod/csd_tournier.py @@ -1,5 +1,4 @@ import numpy as np -from .construct_observation_matrix import construct_model_based_A_matrix from dipy.data import get_sphere, HemiSphere from dipy.reconst.shm import real_sym_sh_mrtrix from dipy.utils.optpkg import optional_package diff --git a/dmipy/tissue_response/tests/test_tissue_response_models.py b/dmipy/tissue_response/tests/test_tissue_response_models.py index f1528d4b..bb944c26 100644 --- a/dmipy/tissue_response/tests/test_tissue_response_models.py +++ b/dmipy/tissue_response/tests/test_tissue_response_models.py @@ -98,8 +98,8 @@ def test_tissue_response_model_multi_compartment_models(): test_data = [test_mc_data, test_mc_data_sm] params = { - 'partial_volume_0': [0.5], - 'partial_volume_1': [0.5], + 'partial_volume_0': 0.5, + 'partial_volume_1': 0.5, 'AnisotropicTissueResponseModel_1_mu': np.array( [np.pi / 2, np.pi / 2]) } diff --git a/examples/example_ivim.ipynb b/examples/example_ivim.ipynb new file mode 100644 index 00000000..2debe719 --- /dev/null +++ b/examples/example_ivim.ipynb @@ -0,0 +1,607 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Intravoxel incoherent motion (IVIM) imaging" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Intra-voxel incoherent motion (IVIM) is a 2-compartment model that separates diffusion signal contributions originating from blood flow and Brownian diffusion *(Le Bihan et al. 1988)*. The model consists of 2 Ball compartments (isotropic Gaussian), each fitting the blood flow and diffusion volume fractions and diffusivities, respectively. Changes in e.g. blood volume fraction has been linked to many pathologies such as the vasculature in tumor tissue *(Le Bihan 2018)*.\n", + "\n", + "\\begin{align}\n", + " E_{\\textrm{IVIM}}= \\underbrace{f_{\\textrm{blood}}\\overbrace{E_{\\textrm{iso}}(\\lambda_{\\textrm{Blood}})}^{\\textrm{Ball}}}_{\\textrm{Blood}} + \\underbrace{f_{\\textrm{Diffusion}}\\overbrace{E_{\\textrm{iso}}(\\cdot|\\lambda_{\\textrm{Diffusion}})}^{\\textrm{Ball}}}_{\\textrm{Diffusion}}\n", + "\\end{align}\n", + "\n", + "Because the apparent diffusivity of blood flow is much higher than that of Brownian motion, the optimization bounds for the diffusivities of the two Balls are disjoint; the diffusivies of the diffusion compartment range\n", + "between [0.5 - 6]e-3 $mm^2/s$ (results in more precise fit according to *(Gurney-Champion et al. 2016)*), and those of the blood compartment range between [6 - 20]e-3 $mm^2/s$ (following *(Park et al. 2017)*). \n", + "\n", + "The separability of blood and diffusion signal hinges on the observation that the blood-flow signal is negligible at b-values above 200-400 s/mm^2, but it does have a constribution below that bvalue (and to the b0).\n", + " \n", + "Many different optimization strategies have been proposed to fit the IVIM model *(Wong et al. 2018, Gurney-Champion et al. 2018)*, of which in this example we will use Dmipy to implement and fit two:\n", + "- Following *(Wong et al. 2018)*, a two-step optimization based on the approach that first fits the 'diffusion' diffusivity by fitting a single Ball compartment to the signal where all b-values below b=400$s/mm^2$ have been truncated. Fixing this initial diffusivity, the 2-compartment model is then fitted to the whole signal.\n", + "- Following *(Gurney-Champion et al. 2018)*, they found simply fixing $\\lambda_{blood}=7e-9 mm^2/s$ results in the second-best IVIM fitting performance (after fancy Bayesian fitting).\n", + "\n", + "We compare our implemented IVIM algorithms with the one available in Dipy, and evaluate/compare the fitted parameter maps and fitting errors." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementing 2-step IVIM using Dmipy\n", + "The 2-step fitting approach used in e.g. *(Wong et al. 2018)* consists of two steps:\n", + "- step 1: fit only the \"diffusion\" part of the data using a single Ball compartment, so the data is truncated to only include measurements above the bvalue_threshold value. This step estimates the \"diffusion\" S0 (which is lower or equal to the actual SO) and the \"diffusion\" diffusivity of this compartment.\n", + "- step 2: fit the 2-compartment model to the whole signal, but fixing the \"diffusion\" diffusivity to the value estimated in step 1.\n", + "\n", + "We'll use the same example dataset and acquisition scheme that Dipy uses as well:\n", + "\n", + "### Load IVIM acquisition scheme and data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset is already in place. If you want to fetch it again please first remove the folder /home/rutger/.dipy/ivim \n", + "Acquisition scheme summary\n", + "\n", + "total number of measurements: 21\n", + "number of b0 measurements: 1\n", + "number of DWI shells: 20\n", + "\n", + "shell_index |# of DWIs |bvalue [s/mm^2] |gradient strength [mT/m] |delta [ms] |Delta[ms] |TE[ms]\n", + "0 |1 |0 |N/A |N/A |N/A |N/A \n", + "1 |1 |10 |N/A |N/A |N/A |N/A \n", + "2 |1 |20 |N/A |N/A |N/A |N/A \n", + "3 |1 |30 |N/A |N/A |N/A |N/A \n", + "4 |1 |40 |N/A |N/A |N/A |N/A \n", + "5 |1 |60 |N/A |N/A |N/A |N/A \n", + "6 |1 |80 |N/A |N/A |N/A |N/A \n", + "7 |1 |100 |N/A |N/A |N/A |N/A \n", + "8 |1 |120 |N/A |N/A |N/A |N/A \n", + "9 |1 |140 |N/A |N/A |N/A |N/A \n", + "10 |1 |160 |N/A |N/A |N/A |N/A \n", + "11 |1 |180 |N/A |N/A |N/A |N/A \n", + "12 |1 |200 |N/A |N/A |N/A |N/A \n", + "13 |1 |300 |N/A |N/A |N/A |N/A \n", + "14 |1 |400 |N/A |N/A |N/A |N/A \n", + "15 |1 |500 |N/A |N/A |N/A |N/A \n", + "16 |1 |600 |N/A |N/A |N/A |N/A \n", + "17 |1 |700 |N/A |N/A |N/A |N/A \n", + "18 |1 |800 |N/A |N/A |N/A |N/A \n", + "19 |1 |900 |N/A |N/A |N/A |N/A \n", + "20 |1 |1000 |N/A |N/A |N/A |N/A \n" + ] + } + ], + "source": [ + "from dipy.data.fetcher import read_ivim\n", + "from dmipy.core.acquisition_scheme import gtab_dipy2dmipy, acquisition_scheme_from_bvalues\n", + "img, gtab = read_ivim()\n", + "scheme_ivim = gtab_dipy2dmipy(gtab, b0_threshold=1e6, min_b_shell_distance=1e6)\n", + "scheme_ivim.print_acquisition_info\n", + "\n", + "data = img.get_data()\n", + "data_slice = data[90: 155, 90: 170, 33, :]\n", + "test_voxel = data_slice[0, 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that this scheme has 1 b-value per \"shell\" for different b-values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1: Fit only Diffusion model using higher b-value data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first step is to truncate the data below a lower b-value threshold and fit a single-compartment Ball model to estimate the \"diffusion\" signal diffusivity.\n", + "\n", + "Note that to do this we need to estimate the S0 intensity of the signal without having any b0-measurements. This is handled internally by dmipy by fitting a Gaussian to the available data points and extrapolating the b0." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "bvalue_threshold = 4e8 # s/m^2 lower threshold b-value\n", + "bvalue_mask = scheme_ivim.bvalues > bvalue_threshold\n", + "gaussian_acquisition_scheme = acquisition_scheme_from_bvalues(\n", + " bvalues=scheme_ivim.bvalues[bvalue_mask],\n", + " gradient_directions=scheme_ivim.gradient_directions[\n", + " bvalue_mask])\n", + "gaussian_data = test_voxel[bvalue_mask]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we must set `optimize_S0=True` here to explicity estimate it." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using parallel processing with 8 workers.\n", + "Setup brute2fine optimizer in 0.00290489196777 seconds\n", + "Fitting of 1 voxels complete in 0.0232429504395 seconds.\n", + "Average of 0.0232429504395 seconds per voxel.\n" + ] + } + ], + "source": [ + "from dmipy.signal_models.gaussian_models import G1Ball\n", + "from dmipy.core.modeling_framework import MultiCompartmentModel\n", + "\n", + "gaussian_mod = MultiCompartmentModel([G1Ball()])\n", + "gaussian_mod.set_parameter_optimization_bounds(\n", + " 'G1Ball_1_lambda_iso', [0.5e-9, 6e-9]) # optimization range according to Gurney-Champion 2016\n", + "gaussian_fit = gaussian_mod.fit(\n", + " acquisition_scheme=gaussian_acquisition_scheme,\n", + " data=gaussian_data,\n", + " optimize_S0=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2: Fit whole signal with 2-compartment IVIM model (D fixed from step 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second step is done by fixing the diffusion diffusivity in the 2-compartment Ball model and fitting the entire signal." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using parallel processing with 8 workers.\n", + "Setup brute2fine optimizer in 0.00647616386414 seconds\n", + "Fitting of 1 voxels complete in 0.0268499851227 seconds.\n", + "Average of 0.0268499851227 seconds per voxel.\n" + ] + } + ], + "source": [ + "ivim_mod = MultiCompartmentModel([G1Ball(), G1Ball()])\n", + "ivim_mod.set_parameter_optimization_bounds(\n", + " 'G1Ball_2_lambda_iso', [6e-9, 20e-9]) # Following Gurney-Champion, 2018\n", + "ivim_mod.set_fixed_parameter(\n", + " parameter_name='G1Ball_1_lambda_iso',\n", + " value=gaussian_fit.fitted_parameters['G1Ball_1_lambda_iso'])\n", + "ivim_fit_2step = ivim_mod.fit(\n", + " acquisition_scheme=scheme_ivim,\n", + " data=test_voxel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementing Fixed Dstar IVIM using Dmipy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fixed D-star IVIM implementation is very simple. We set the blood diffusivity to 7e-9 $m^2/s$ and fit the model as usual." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ivim_mod = MultiCompartmentModel([G1Ball(), G1Ball()])\n", + "ivim_mod.set_fixed_parameter(\n", + " 'G1Ball_2_lambda_iso', 7e-9) # Following Gurney-Champion 2016\n", + "ivim_mod.set_parameter_optimization_bounds(\n", + " 'G1Ball_1_lambda_iso', [.5e-9, 6e-9]) # Following Gurney-Champion 2016\n", + "ivim_fit_Dfixed = ivim_mod.fit(\n", + " acquisition_scheme=scheme_ivim,\n", + " data=test_voxel,\n", + " verbose=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also fit the Dipy IVIM implementation as a reference" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from dipy.reconst.ivim import IvimModel\n", + "ivimmodel = IvimModel(gtab)\n", + "ivim_fit_dipy = ivimmodel.fit(test_voxel)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we can visualize the signal fits to this test voxel for the three different IVIM algorithms. Note they're all very similar." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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S7N69m2uvvdZr9XuCkJAQEhMTfXpemrPnKCKblVKeW3dfTArXiTwXZ2md0HgG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\nSkQeFJF+lzd3BOfrAgKE93s2IqpyMZ6ZFUvMH2e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vg8mdbTNPlfJTkRHhTH64GUmpGfSb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\ngEqVKjX9888/XfxbKXdatuM4z8yMBeCD+xtzR90ysHcZzHnYdkDPiVC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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "fig, axs = plt.subplots(ncols=3, figsize=[10, 4], sharey=True)\n", + "fig.suptitle('Test signal fit comparisons IVIM algorithms', fontsize=15)\n", + "axs[0].set_ylabel('Signal Intensity')\n", + "axs[0].set_xlabel('b-value [s/m^2]')\n", + "axs[1].set_xlabel('b-value [s/m^2]')\n", + "axs[2].set_xlabel('b-value [s/m^2]')\n", + "\n", + "axs[0].scatter(scheme_ivim.bvalues, test_voxel, label='Measured DWIs')\n", + "axs[0].plot(scheme_ivim.bvalues, gaussian_fit.predict(scheme_ivim)[0], label='Step 1 Fit')\n", + "axs[0].plot(scheme_ivim.bvalues, ivim_fit_2step.predict()[0], label='Step 2 Fit')\n", + "axs[1].scatter(scheme_ivim.bvalues, test_voxel, label='Measured DWIs')\n", + "axs[1].plot(scheme_ivim.bvalues, ivim_fit_Dfixed.predict()[0], label='Dstar Fixed Fit')\n", + "axs[2].scatter(scheme_ivim.bvalues, test_voxel, label='Measured DWIs')\n", + "axs[2].plot(scheme_ivim.bvalues, ivim_fit_dipy.predict(gtab), label='Dipy IVIM reference')\n", + "for ax in axs:\n", + " ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameter map comparison IVIM 2-step, Dstar_fixed, and Dipy reference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To properly evaluate the three algorithms we fit them to the same example slice as in the dipy IVIM example.\n", + "\n", + "Note that in practice we can import custom (prepared) multi-compartment models directly:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting step 1 of IVIM 2-step algorithm.\n", + "Starting step 2 of IVIM 2-step algorithm.\n", + "Cannot estimate signal grid with voxel-dependent x0_vector.\n", + "IVIM 2-step optimization of 5200 voxels complete in 35.485 seconds\n", + "Starting IVIM Dstar-fixed algorithm.\n", + "IVIM Dstar-fixed optimization of 5200 voxels complete in 16.440 seconds\n", + "Dipy computation time: 149.582684994 s\n" + ] + } + ], + "source": [ + "from dmipy.custom_optimizers.intra_voxel_incoherent_motion import ivim_2step, ivim_Dstar_fixed\n", + "from time import time\n", + "ivim_fit_dmipy_2step = ivim_2step(scheme_ivim, data_slice)\n", + "ivim_fit_dmipy_fixed = ivim_Dstar_fixed(scheme_ivim, data_slice)\n", + "dipy_start = time()\n", + "ivim_fit_dipy = ivimmodel.fit(data_slice)\n", + "print('Dipy computation time: {} s'.format(time() - dipy_start))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then visualize the fitted parameter maps together:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Iv7bucOABVd0CoKp3isjRwDP952m4Wcr3ichTVfXPDRMka1z9DDgK+DRwDU67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\nW3fX3ZkPP++/s/XIcw2NyVr4gTsK65fZwK1xt77+xXDf8fZltjLlazEpx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hdX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\n8TXU73qmO42x9cPGg0ZVrwAdft2mNftOpD9xU+NI3Nq5L46zSM7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k8XBtqhPP7/89lKjU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cpVSYhCWTWXI7OVin9QZu3Z7PfzsNLlmP62HgibSsPayjejd3gFe9hZvveQYA4K+eflmx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\ncDgcDofD4XA8gvj/Af9rJ1m+LPEVAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "fig, axs = plt.subplots(nrows=4, ncols=3, figsize=[15, 16])\n", + "fig.suptitle('IVIM Parameter Map Comparison', fontsize=25, y=0.93)\n", + "axs = axs.ravel()\n", + "axs[0].set_title('Dmipy 2-Step', fontsize=18)\n", + "axs[1].set_title('Dmipy Dstar-Fixed', fontsize=18)\n", + "axs[2].set_title('Dipy', fontsize=18)\n", + "axs[0].set_ylabel('S0-Predicted', fontsize=15)\n", + "axs[3].set_ylabel('perfusion fraction', fontsize=15)\n", + "axs[6].set_ylabel('D_star (perfusion)', fontsize=15)\n", + "axs[9].set_ylabel('D (diffusion)', fontsize=15)\n", + "\n", + "args = {'vmin': 0., 'interpolation': 'nearest'}\n", + "im0 = axs[0].imshow(ivim_fit_dmipy_2step.S0, **args)\n", + "im1 = axs[1].imshow(ivim_fit_dmipy_fixed.S0, **args)\n", + "im2 = axs[2].imshow(ivim_fit_dipy.S0_predicted, **args)\n", + "im3 = axs[3].imshow(ivim_fit_dmipy_2step.fitted_parameters['partial_volume_1'], vmax=1., **args)\n", + "im4 = axs[4].imshow(ivim_fit_dmipy_fixed.fitted_parameters['partial_volume_1'], vmax=1., **args)\n", + "im5 = axs[5].imshow(ivim_fit_dipy.perfusion_fraction, vmax=1., **args)\n", + "im6 = axs[6].imshow(ivim_fit_dmipy_2step.fitted_parameters['G1Ball_2_lambda_iso'] * 1e9, vmax=20, **args)\n", + "im7 = axs[7].imshow(np.ones_like(ivim_fit_dmipy_2step.S0) * \n", + " ivim_fit_dmipy_fixed.fitted_and_linked_parameters['G1Ball_2_lambda_iso'] * 1e9, vmax=20, **args)\n", + "axs[7].text(10, 10, 'Fixed to 7e-9 mm^2/s', fontsize=14, color='white')\n", + "im8 = axs[8].imshow(ivim_fit_dipy.D_star * 1e3, vmax=20, **args)\n", + "im9 = axs[9].imshow(ivim_fit_dmipy_2step.fitted_parameters['G1Ball_1_lambda_iso'] * 1e9, vmax=6, **args)\n", + "im10 = axs[10].imshow(ivim_fit_dmipy_fixed.fitted_parameters['G1Ball_1_lambda_iso'] * 1e9, vmax=6, **args)\n", + "im11 = axs[11].imshow(ivim_fit_dipy.D * 1e3, vmax=6, **args)\n", + "\n", + "for im, ax in zip([im0, im1, im2, im3, im4, im5, im6, im7, im8, im9, im10, im11], axs):\n", + " fig.colorbar(im, ax=ax, shrink=0.7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the 2 Dmipy algorithms perform very similarly, meaning that fixing Dstar apparently does not influence the optimized values of other values much (as expected, otherwise this would not be the finding of *(Gurney-Champion 2016)*.\n", + "\n", + "Interestingly, the Dipy IVIM algorithm finds overall higher perfusion volume fractions than either Dmipy implementation, as well as extremely high D-star values outside of the optimization range.\n", + "\n", + "Our findings become more clear in the following parameter histograms in the example slice:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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5f9q0acydO5df/OIXRKOpJ7t89tln+d73vkd5eTnTp0+nqamJbdtcU8zZZ5/N\n0KFDKS4u5uKLL+all17KcDeMyVNvrHTf339+37ZEkNZ7mbR0CbNs+6SlYnVY/2B90ozpoC1btuD3\n+xkxwn26LiwsTOzz+XyJ330+H5FIJOU1kjsBx3+eNm0aW7du5YUXXiAajXL88cenPHf16tXcdddd\nvPDCC4nH+uY3v5n4BLtp0yZ+/vOfs3btWp566inKy8sT/VGSqSqPP/44xxxzTIvta9eubdNJuR+P\nsjSm85pdVoaCQfu2JZo7eyeT1l4urTOjO60O6z8sk2ZMB1RWVnL99dczb968Lv3TxzvKrlixgpNP\nPjmx/eqrr2b27NlpP4Fu3LiRr3zlK6xatSpRwQLcddddbNq0KVGRvf/++0ydOpWFCxcybNgwPvzw\nQ0pKSqitrU2cM2PGDBYvXpz4RL1x48bEvj/+8Y9UVVXR2NjIypUrmTZtWqefqzH9VnMdiB8C+4KY\nvsikpZsQrTM1kNVh/Ytl0oxpR2NjI+Xl5YTDYQKBAHPmzOGWW27p0jVDoRBTp04lFovxm9/8JrH9\nqquu4vbbb080JbQ2f/586urquOyyywAYNWoUq1atSnnce++9h6py1llnMWHCBEaNGpVoGvjGN77B\nt771LW666SbGjx+PqjJ69GiefPJJAE499VTmzJnD5s2bufLKK5k0aVKXnq8x/VK4AQoGtmxvDBQB\n0q/6pFkd1n/rMOlMp8NcM2nSJO2vnQK7W75NwfHWW29x3HHH9XUxutXo0aPZsGEDw4YNa7Pvscce\n44knnuChhx7qg5I5y5YtY8OGDSxZsqTPypAs1WtARF5V1f5Z67Zi9VcOSF4sfVJSBujnn3HTcJx+\na8vjn/kGjPw0XPNUu1NwdLUO21m3k+pQNSMG7ss6xZeF+qD6AwRhdNnoTl+/M6wO65iu1GGWScsj\nj777KBv3ViV+nzg49agZk5tuvPFGfv/73+93kzUak7Oa610mrbXgAIj0ViZNM7Zr5tKKA1aHdT8L\n0ozpZelGSy1evLh3C5LG3LlzmTt3bl8Xw5i+11zfcrWBuGBxLw4cSE+QPgnSrA7rPTZwwBhjjEml\nuS59Jq03p+DIkErLpUya6X5ZBWkicq6IvCMim0XkthT7C0Vkhbd/rYiMTtr3DW/7OyIyo71rinOX\niLwrIm+JyFe79hSNMcaYDlLdN3CgtWBxIkjbsbeR5kjH1sHsUDEyBGEi0v7IAtOvtdvcKSJ+4CfA\n2cB2YL2VuJ4MAAAgAElEQVSIrFLVN5MO+xLwiaoeKSJXAN8HLheRscAVwDhgJLBaRI72zkl3zbnA\np4BjVTUmIklTPZuMtr7C8Kr6fb9bnzRjjOmcpmrQWNpMWnNTPd9/8k3uf+kDAj5h/KfKOHHUAd1f\nDu3apLWmf8smkzYF2KyqW1S1GVgOzGx1zEzgV97PjwFniZuAZSawXFVDqvoBsNm7XqZr/guwUFVj\nAKpa0fmnZ4wxxnRCozcIK1WQFigmEG3i3Y9r8AlEYspPn9/MI2u3dXgB9fb0p4EDpvtlE6QdAnyY\n9Pt2b1vKY1Q1AlQDQzOcm+maR+CycBtE5PciclSqQonIdd4xGyorK7N4GsZ0jt/vp7y8nHHjxjFh\nwgQWLVpELNax5o077riD1atXd7ksCxYs4JBDDqG8vJyjjjqKiy++mDfffDPjOStXrmz3mGxMnz6d\nY445hvLycsrLyxOLLZ9yyildvja0XMDZmD7X4AVpwRRBWkExPo3yYUUVhw0dyLBBhezY29RjRUmX\nScs2w2Z1mNMf67BsRnemehW0Dt3THZNue6rgMH7NQqBJVSeJyMXAL4HPtDlYdSmwFNw8Q6mLbvJO\n8nxG3WFS6lmxkxUXFydmwa6oqODKK6+kurqaO++8M+uHWbhwYaeL2NrNN9/M17/+dcDN9n3mmWfy\n97//neHDh6c8fuXKlVxwwQWMHTs268eIRCIEAm2rh4cffrjNpJCvvPJKB0pvTO5Z+8G+qYumxl/e\nDXvc93QDB4CGmiomjB3BrpomtlVlOZCgg3XYgNAnBKNhCpPLEXA/D2yq4pPjWzdstWV12D79rQ7L\nJpO2HddHLO5QYEe6Y0QkAJQBVRnOzXTN7cDj3s+/A8ZnUUZjesWIESNYunQpS5YsQVVZtmwZs2bN\n4sILL2TMmDEsWbKERYsWMXHiRE466SSqqlzln/wJa/To0dx6661MmTKFKVOmsHnzZmpraxkzZgzh\ncBiAmpoaRo8enfg9ncsvv5xzzjmHRx55BIDbbruNsWPHMn78eL7+9a/zyiuvsGrVKubPn095eTnv\nv/8+v/jFL5g8eTITJkzgkksuoaGhIVHGW265hTPOOINbb70108O2MGiQW9fwd7/7HZ/97GdRVXbu\n3MnRRx/Nxx9/TDQaZf78+UyePJnx48dz7733Am6m9Hnz5jF27FjOP/98KiqsZ4PJIQ0Zmju9paHK\npJ4jhg1kZFkxexvCNIRSr3PZVWkzaZ0YOGB1WFu5XIdlk0lbDxwlImOAj3ADAa5sdcwq4AvAX4BL\ngedUVUVkFfCIiCzCDRw4CliHy7Clu+ZK4ExcBu104N3OP7392xHbHnU/+N3s1NlkjUz7Dj/8cGKx\nWOIf8vXXX2fjxo00NTVx5JFH8v3vf5+NGzdy88038+CDD3LTTTe1uUZpaSnr1q1L7H/yySeZPn06\nTz31FLNmzWL58uVccsklBIPBdstz4okn8vbbb1NVVcXvfvc73n77bUSEvXv3MnjwYC666CIuuOAC\nLr30UgAGDx7Ml7/8ZQBuv/127r//fm688UYA3n33XVavXo3f70/5WFdddRXFxe4N6k9/+hNDhw5N\n7Pv85z/P448/zk9+8hP+8Ic/cOedd3LQQQexdOlSysrKWL9+PaFQiGnTpnHOOeewceNG3nnnHf7+\n97+za9cuxo4dyxe/+MUO/CWM6UGZMmkB9z9QQgMHlhXRHHWR0o7qJo4cMajt8V2g2v190qwO6z91\nWLtBmqpGRGQe8AzgB36pqm+IyEJgg6quAu4HHhKRzbgM2hXeuW+IyG+BN4EIcIOqRgFSXdN7yO8B\nD4vIzUAdcG33Pd39ywuRdwDYVuUqmcv6sjB5Jnk5tTPOOIOSkhJKSkooKyvjwgsvBOCEE07gtdde\nS3l+fF272bNnc/PNNwNw7bXX8oMf/IBZs2bxwAMP8Itf/KJDZSktLaWoqIhrr72W888/nwsuuCDl\n8a+//jq33347e/fupa6ujhkzEjPjcNlll6Wt3CB1U0GyxYsXc/zxx3PSSSclnuOzzz7La6+9lvgU\nXl1dzXvvvceLL77I7Nmz8fv9jBw5kjPPPDOr52tMr2jY7S2uXtR2X9BtG1HYTGHAz0Fl7vddNd0f\npGXSlTGfVoellmt1WFYrDqjq08DTrbbdkfRzE2liAFW9C7grm2t62/cC52dTLmP6wpYtW/D7/YwY\n4WaHKSwsTOzz+XyJ330+H5FI6uYPSVqwOf7ztGnT2Lp1Ky+88ALRaJTjjz8+q/Js3LiRSZMmEQgE\nWLduHX/6059Yvnw5S5Ys4bnnnmtz/Ny5c1m5ciUTJkxg2bJlrFmzJrFv4MB9WYMZM2awa9cuJk2a\nxH333ZdVWT766CN8Ph+7du0iFovh8/lQVRYvXtyiIgV4+umnW9wHY3JKfSUUDmq5uHqcF7gdXOia\n8gYW+An4hNqmzE17PaEzmTSrw9LLtTrMVhwwpgMqKyu5/vrrmTdvXpf+OVesWJH4fvLJJye2X331\n1cyePZtrrsmuafrxxx/n2WefZfbs2dTV1VFdXc3nPvc57rnnnkRH4ZKSEmpraxPn1NbWcvDBBxMO\nh3n44YfTXvuZZ55h06ZNWVdukUiEa665hkceeYTjjjuORYsWAa6i/NnPfpbom/Luu+9SX1/Paaed\nxvLly4lGo+zcuZPnn38+q8cxplfU74aCktT7vCBtREEz4IKUQUUBapp6t08adLxPmtVh6eViHWZr\ndxrTjsbGRsrLywmHwwQCAebMmcMtt9zSpWuGQiGmTp1KLBbjN7/5TWL7VVddxe23355Is6dy9913\n8+tf/5r6+nqOP/54nnvuOYYPH87OnTuZOXMmTU1NqCp33303AFdccQVf/vKX+fGPf8xjjz3Gt7/9\nbaZOncphhx3GCSec0KLy64rvfOc7fOYzn+Ezn/kM5eXlTJ48mfPPP59rr72WrVu3cuKJJ6KqDB8+\nnJUrV/L5z3+e5557jhNOOIGjjz6a008/vVvKYUy3iGfSUqiOFVMGDCtoJp47Ky0K9kgmLblZMuX+\nLKI0q8Oyk4t1mLT3AugPJk2apBs2bOjrYvS5R5+9mQ/21LfZPmao1yftnLt7u0hd9tZbb3Hcccf1\ndTG61ejRo9mwYQPDhg1rs++xxx7jiSee4KGHHuqDkuWmVK8BEXlVVdN3LOlHrP7qe2sf/WHi56mX\nfc39cM8JMHAETPznNsf/bY+PCX/5Ks8deA0fn+iCnYfX/oPK2hA3ffZorpw6KnFsV+uwrdVbUZSy\nwrLEtiFFbjDYjrod1DbXcsyQYzp9/c6wOqxjulKHWSbNmBxx44038vvf/56nn27TVdMY09vqd8OQ\nI1Lu2lof5Agtoswf4mNvW0lRgPcr63qvfJ5cWnHA6rDuZ0GaMb1s69atKbcvXry4dwtijEktVOct\nrp66uXN7g586iimhMbGttChIUzhGONq9i60rmnNrd1od1nts4IAxxhiTrN5bajBNn7SPG300UExh\nbF/3kpIil/Oo7ebBA5kyZdKJgQOmf7EgzRhjjElWv9t9TzO6c2eDnyYpJBhJDtLcpK3dPnhAST8K\nU3KrudN0P2vuNMaYbiYiW4F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SbJ60/GdBWp6pjdUxKhyhKjiUaEGAMaECdhY0EuuGdeSM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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "fig, axs = plt.subplots(2, 2, figsize=[10, 9])\n", + "fig.suptitle('Comparison Parameter Histograms', fontsize=20)\n", + "axs = axs.ravel()\n", + "sns.distplot(ivim_fit_dmipy_2step.S0.ravel(), ax=axs[0], label='Dmipy 2-step')\n", + "sns.distplot(ivim_fit_dmipy_fixed.S0.ravel(), ax=axs[0], label='Dmipy Dstar-Fixed')\n", + "sns.distplot(ivim_fit_dipy.S0_predicted.ravel(), ax=axs[0], label='Dipy Reference')\n", + "axs[0].set_title('S0')\n", + "\n", + "sns.distplot(ivim_fit_dmipy_2step.fitted_parameters['partial_volume_1'].ravel(), ax=axs[1], label='Dmipy 2-step')\n", + "sns.distplot(ivim_fit_dmipy_fixed.fitted_parameters['partial_volume_1'].ravel(), ax=axs[1], label='Dmipy Dstar-Fixed')\n", + "sns.distplot(ivim_fit_dipy.perfusion_fraction.ravel(), ax=axs[1], label='Dipy Reference')\n", + "axs[1].set_title('Perfusion Fraction')\n", + "\n", + "sns.distplot(ivim_fit_dmipy_2step.fitted_parameters['G1Ball_2_lambda_iso'].ravel() * 1e9, ax=axs[2], label='Dmipy 2-step')\n", + "axs[2].axvline(x=7, label='Dmipy Dstar-Fixed')\n", + "sns.distplot(ivim_fit_dipy.D_star.ravel() * 1e3, ax=axs[2], label='Dipy Reference')\n", + "axs[2].set_ylim(0, 0.005)\n", + "axs[2].set_title('D_star (perfusion)')\n", + "axs[2].text(450, 0.001, 'Dipy IVIM does not respect\\noptimization boundaries')\n", + "axs[2].arrow(800, 0.0005, 100, -0.0001, width=0.00005, head_length=80.)\n", + "\n", + "sns.distplot(ivim_fit_dmipy_2step.fitted_parameters['G1Ball_1_lambda_iso'].ravel() * 1e9, ax=axs[3], label='Dmipy 2-step')\n", + "sns.distplot(ivim_fit_dmipy_fixed.fitted_parameters['G1Ball_1_lambda_iso'].ravel() * 1e9, ax=axs[3], label='Dmipy Dstar-Fixed')\n", + "sns.distplot(ivim_fit_dipy.D.ravel() * 1e3, ax=axs[3], label='Dipy Reference')\n", + "axs[3].set_title('D (diffusion)')\n", + "\n", + "axs[0].legend()\n", + "axs[1].legend()\n", + "axs[2].legend()\n", + "axs[3].legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the histograms notice again that the 2 Dmipy implementations find similar parameter values, and the Dipy implementation differs.\n", + "- S0 is basically the same for all algorithms.\n", + "- Perfusion fraction very similar for Dmipy IVIMs, and Dipy IVIM finds a very particular peak just above 0.25.\n", + "- D_star values for Dmipy fall within the optimization ranges, but it's impossible to see with the Dipy D_star values, which apparently sometimes find value sof 1000 (i.e. 3000 mm^2/s, 1000 times free water diffusivity).\n", + "- The regular D estimation for Dmipy IVIMs are very similar again, with Dipy IVIM being somewhat lower overall." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fitting error comparison" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Following our previous findings we can also calculate the mean squared fitting error for the three algorithms." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "mse_2step = ivim_fit_dmipy_2step.mean_squared_error(data_slice)\n", + "mse_Dstar_fixed = ivim_fit_dmipy_fixed.mean_squared_error(data_slice)\n", + "mse_dipy = np.mean(\n", + " (ivim_fit_dipy.predict(gtab) / ivim_fit_dipy.S0_predicted[..., None] - \n", + " data_slice / ivim_fit_dipy.S0_predicted[..., None]) ** 2, axis=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OGmfRNcw7U46zCjDETutoAFjHcZwSFl3Dut+ZmtS0mTKDD9TXII7RSt8JMz/L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F8LHEu4tfl1Znao2RqO40weTTZTx0m6JXm+2cw2J8+XAfclz3cA59StURkTCyPvA5YQ8p3OAa4G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\nNtXV21UST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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(nrows=2, ncols=2, figsize=[10, 8])\n", + "fig.suptitle('IVIM fitting error comparison', fontsize=20)\n", + "axs = axs.ravel()\n", + "im0 = axs[0].imshow(mse_2step, vmax=0.08)\n", + "im1 = axs[1].imshow(mse_Dstar_fixed, vmax=0.08)\n", + "im2 = axs[2].imshow(mse_dipy, vmax=0.08)\n", + "\n", + "axs[0].set_title('Dmipy IVIM 2-step')\n", + "axs[1].set_title('Dmipy IVIM Dstar-Fixed')\n", + "axs[2].set_title('Dipy IVIM reference')\n", + "\n", + "for im, ax in zip([im0, im1, im2], axs):\n", + " fig.colorbar(im, ax=ax, shrink=0.7)\n", + "\n", + "axs[3].boxplot(\n", + " x=[mse_2step.ravel(), mse_Dstar_fixed.ravel(), mse_dipy.ravel()],\n", + " labels=['Dmipy 2-step', 'Dmipy D-fixed', 'Dipy reference']);\n", + "axs[3].set_ylabel('Mean Squared Error')\n", + "axs[3].set_title('Fitting Error Boxplots', fontsize=13);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that Dmipy 2-step and D-fixed have very similar fitting error, with 2-step having 2 outliers (local minima could possibly be avoided by increasing brute-force sampling rate Ns). The Dipy IVIM has significantly higher fitting error compared to the Dmipy IVIMs, even with some extreme outliers.\n", + "\n", + "This example demonstrated that Dmipy can be easily used to generate, fit and evaluate differet IVIM implementations, and that it's performance is better and faster than the Dipy reference implementation at the moment of this writing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### References\n", + "- Le Bihan, D., Breton, E., Lallemand, D., Aubin, M. L., Vignaud, J., & Laval-Jeantet, M. (1988). Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology, 168(2), 497-505.\n", + "- Le Bihan, D. (2017). What can we see with IVIM MRI?. NeuroImage\n", + "- Gurney-Champion OJ, Froeling M, Klaassen R, Runge JH, Bel A, Van Laarhoven HWM, et al. Minimizing the Acquisition Time for Intravoxel Incoherent Motion Magnetic Resonance Imaging Acquisitions in the Liver and Pancreas. Invest Radiol. 2016;51: 211–220.\n", + "- Park HJ, Sung YS, Lee SS, Lee Y, Cheong H, Kim YJ, et al. Intravoxel incoherent motion diffusion-weighted MRI of the abdomen: The effect of fitting algorithms on the accuracy and reliability of the parameters. J Magn Reson Imaging. 2017;45: 1637–1647.\n", + "- Wong, S. M., Backes, W. H., Zhang, C. E., Staals, J., van Oostenbrugge, R. J., Jeukens, C. R. L. P. N., & Jansen, J. F. A. (2018). On the Reproducibility of Inversion Recovery Intravoxel Incoherent Motion Imaging in Cerebrovascular Disease. American Journal of Neuroradiology.\n", + "- Gurney-Champion, O. J., Klaassen, R., Froeling, M., Barbieri, S., Stoker, J., Engelbrecht, M. R., ... & Nederveen, A. J. (2018). Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients. PloS one, 13(4), e0194590." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/example_verdict.ipynb b/examples/example_verdict.ipynb index 2a22d4a7..b27ab2f6 100644 --- a/examples/example_verdict.ipynb +++ b/examples/example_verdict.ipynb @@ -40,10 +40,17 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rutger/anaconda2/lib/python2.7/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n" + ] + } + ], "source": [ "from dmipy.signal_models import sphere_models, cylinder_models, gaussian_models" ] @@ -117,7 +124,7 @@ "outputs": [ { "data": { - "image/png": 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0VMj8zjvvUJcuXejnn38WrnvQY1QqFa1YsUJoZ968eXTu3Dm9/k2Jbnca48eP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\n3rx52Lt3L65evYoePXqIEkNLlpCQgAEDBsDOzg6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3ce/ePYwYMQJ37tzBoEGDsHv37hrH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\n", 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HDh0SO06XVV1dTXPnziUA9OKLL7brPaOsc9uwYQPZ29uTi4sLrVu3jkdgO5ns7GyaOHGi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3nppZdQSvHzzz/z/vvv89VXX7F7926CgoLYsmULmZmZAAQHB7Nu3To6OzvZuXMnp0+fpqysDKPR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\nQgghhBBCCHEbDf8Hu8BhY/nQTtAAAAAASUVORK5CYII=\n", "text/plain": [ "" ] @@ -149,7 +156,8 @@ "outputs": [], "source": [ "verdict_mod.set_fixed_parameter('G1Ball_1_lambda_iso', 0.9e-9)\n", - "verdict_mod.parameter_ranges['C1Stick_1_lambda_par'] = (3.05, 10)" + "verdict_mod.set_parameter_optimization_bounds(\n", + " 'C1Stick_1_lambda_par', [3.05e-9, 10e-9])" ] }, { @@ -175,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -255,16 +263,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Setup MIX optimizer in 7.86781311035e-06 seconds\n", - "Fitting of 1 voxels complete in 1.97516489029 seconds.\n", - "Average of 1.97516489029 seconds per voxel.\n" + "Setup MIX optimizer in 2.90870666504e-05 seconds\n", + "Fitting of 1 voxels complete in 3.41024303436 seconds.\n", + "Average of 3.41024303436 seconds per voxel.\n" ] } ], @@ -288,14 +296,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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4Dz6Qs9Y51y6mjvC46nXn3Kg02i3GBzYjN83Nx9/g+QX+e3co/vu4FD9eiARA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/MGDtAC4kXLDLtZySfyj0WQ73PQs7voFZj8LN846OSogstcoGseK55nRrWIGvfv6XbpO3cfJy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\n9ozOa1i5IVO7TOWpiKdYd3gd3Rd0Z8bvM8g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pASM3xZXgx6Oz8WOvIvxNdScA3WPHhsHNm8vwY7lS4LdBfZG0eJsS/S0kY2bL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\nABPA8y2e58aLbmTsr2MZv2G81/V4JDAYbnoP2j4Fv86AmbdCgm+jBiW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Sk1LoMe5HPlmz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6TSLmG/UgpUKSUiIpJXBd9QnMAyYcSus9+qD9Kqoy6fOc3uVb/7YGciril6551UfPdd4tav5/DT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RlVsbh3/vpFd6qKEKIghSIqiICDZK6EXAhtLEo9hF9OjBo9+xK1YEREVp0ktAUKqoIE2lWiD0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\nd1TzyO7EnjzOnz//aHM6ERGRvzkCAgh74nF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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -330,7 +338,7 @@ " axs[1].scatter(G_nonzero[mask], data_nonzero[mask], s=3., marker='o')\n", "axs[1].set_title('Dmipy Verdict Results', fontsize=30)\n", "axs[1].set_xlabel('G(T/m)', fontsize=20)\n", - "axs[1].set_ylabel('S', fontsize=20);\n" + "axs[1].set_ylabel('S', fontsize=20);" ] }, { @@ -344,21 +352,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'C1Stick_1_lambda_par': array([ 6.50135504e-09]),\n", - " 'C1Stick_1_mu': array([[ 1.01233485, 0.60671024]]),\n", - " 'S4SphereGaussianPhaseApproximation_1_diameter': array([ 1.54848480e-05]),\n", - " 'partial_volume_0': array([ 0.76624602]),\n", - " 'partial_volume_1': array([ 0.0914321]),\n", - " 'partial_volume_2': array([ 0.14232188])}" + "{'C1Stick_1_lambda_par': array([6.58332016e-09]),\n", + " 'C1Stick_1_mu': array([[ 2.12873298, -2.53619312]]),\n", + " 'S0': array([[1., 1., 1., 1., 1., 1.]]),\n", + " 'S4SphereGaussianPhaseApproximation_1_diameter': array([1.54025803e-05]),\n", + " 'partial_volume_0': array([0.76130392]),\n", + " 'partial_volume_1': array([0.09744434]),\n", + " 'partial_volume_2': array([0.14125174])}" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -394,7 +403,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.14" + "version": "2.7.13" } }, "nbformat": 4,