a base class for LogLikelihood of independent Poisson variables where the mean values are linear combinations of the kinetic parameters. More...

#include "stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h"

Inheritance diagram for stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >:

[legend]

Public Member Functions
TargetT *	construct_target_ptr () const override
	Returns a pointer to a newly allocated target object (with 0 data). More...

std::unique_ptr< ExamInfo >	get_exam_info_uptr_for_target () const override

Functions to get parameters
Warning Be careful with changing shared pointers. If you modify the objects in one place, all objects that use the shared pointer will be affected.
const DynamicProjData &	get_dyn_proj_data () const

const shared_ptr< DynamicProjData > &	get_dyn_proj_data_sptr () const

const int	get_max_segment_num_to_process () const

const bool	get_zero_seg0_end_planes () const

const DynamicProjData &	get_additive_dyn_proj_data () const

const shared_ptr< DynamicProjData > &	get_additive_dyn_proj_data_sptr () const

const ProjectorByBinPair &	get_projector_pair () const

const shared_ptr< ProjectorByBinPair > &	get_projector_pair_sptr () const

const BinNormalisation &	get_normalisation () const

const shared_ptr< BinNormalisation > &	get_normalisation_sptr () const

Functions to set parameters
This can be used as alternative to the parsing mechanism. Warning After using any of these, you have to call set_up(). Be careful with setting shared pointers. If you modify the objects in one place, all objects that use the shared pointer will be affected.
void	set_recompute_sensitivity (const bool)

void	set_sensitivity_sptr (const shared_ptr< TargetT > &)

int	set_num_subsets (const int num_subsets) override
	Attempts to change the number of subsets. More...

void	set_normalisation_sptr (const shared_ptr< BinNormalisation > &) override
	set_normalisation_sptr More...

void	set_additive_proj_data_sptr (const shared_ptr< ExamData > &) override
	set_additive_proj_data_sptr More...

void	set_input_data (const shared_ptr< ExamData > &) override
	set_input_data More...

const DynamicProjData &	get_input_data () const override
	get input data More...

Public Member Functions inherited from stir::RegisteredParsingObject< PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >, GeneralisedObjectiveFunction< TargetT >, PoissonLogLikelihoodWithLinearModelForMean< TargetT > >
std::string	get_registered_name () const override
	Returns Derived::registered_name.

std::string	parameter_info () override
	Returns a string with all parameters and their values, in a form suitable for parsing again.

Public Member Functions inherited from stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >
void	compute_sub_gradient_without_penalty (TargetT &gradient, const TargetT &current_estimate, const int subset_num) override
	Compute the subset gradient of the (unregularised) objective function. More...

virtual void	compute_sub_gradient_without_penalty_plus_sensitivity (TargetT &gradient, const TargetT &current_estimate, const int subset_num)
	This should compute the subset gradient of the (unregularised) objective function plus the subset sensitivity. More...

Succeeded	set_up (shared_ptr< TargetT > const &target_sptr) override
	set-up sensitivity etc if possible More...

const TargetT &	get_sensitivity () const
	Get a const reference to the total sensitivity.

const TargetT &	get_subset_sensitivity (const int subset_num) const
	Get a const reference to the sensitivity for a subset.

bool	get_use_subset_sensitivities () const
	find out if subset_sensitivities are used More...

bool	get_recompute_sensitivity () const
	find current value of recompute_sensitivity

std::string	get_sensitivity_filename () const
	get filename to read (or write) the total sensitivity More...

std::string	get_subsensitivity_filenames () const
	get filename pattern to read (or write) the subset sensitivities More...

void	fill_nonidentifiable_target_parameters (TargetT &target, const float value) const override

void	set_recompute_sensitivity (const bool)

void	set_subset_sensitivity_sptr (const shared_ptr< TargetT > &, const int subset_num)

void	set_use_subset_sensitivities (const bool)
	See get_use_subset_sensitivities()

void	set_sensitivity_filename (const std::string &)
	set filename to read (or write) the total sensitivity More...

void	set_subsensitivity_filenames (const std::string &)
	set filename pattern to read (or write) the subset sensitivities More...

Public Member Functions inherited from stir::GeneralisedObjectiveFunction< TargetT >
virtual void	compute_sub_gradient (TargetT &gradient, const TargetT &current_estimate, const int subset_num)
	Compute the subset-gradient of the objective function at current_estimate. More...

virtual void	compute_gradient (TargetT &gradient, const TargetT &current_estimate)
	Compute the gradient of the objective function at the current_estimate. More...

virtual void	compute_gradient_without_penalty (TargetT &gradient, const TargetT &current_estimate)
	Compute the gradient of the unregularised objective function at the current_estimate. More...

virtual double	compute_objective_function_without_penalty (const TargetT &current_estimate, const int subset_num)
	Compute the value of the unregularised sub-objective function at the current_estimate. More...

virtual double	compute_objective_function_without_penalty (const TargetT &current_estimate)
	Compute the value of the unregularised objective function at the current_estimate. More...

double	compute_penalty (const TargetT &current_estimate, const int subset_num)
	Compute the value of the sub-penalty at the current_estimate. More...

double	compute_penalty (const TargetT &current_estimate)
	Compute the value of the penalty at the current_estimate. More...

double	compute_objective_function (const TargetT &current_estimate, const int subset_num)
	Compute the value of the sub-objective function at the current_estimate. More...

double	compute_objective_function (const TargetT &current_estimate)
	Compute the value of the objective function at the current_estimate. More...

double	compute_value (const TargetT &current_estimate)
	Alias for compute_objective_function(const TargetT&)

std::string	get_objective_function_values_report (const TargetT &current_estimate)
	Construct a string with info on the value of objective function with and without penalty.

int	get_num_subsets () const
	Return the number of subsets in-use.

bool	subsets_are_approximately_balanced () const
	Checks of the current subset scheme is approximately balanced. More...

bool	subsets_are_approximately_balanced (std::string &warning_message) const
	Checks of the current subset scheme is approximately balanced and constructs a warning message. More...

bool	prior_is_zero () const
	check if the prior is set (or the penalisation factor is 0)

GeneralisedPrior< TargetT > *const	get_prior_ptr () const
	Read-only access to the prior. More...

shared_ptr< GeneralisedPrior< TargetT > >	get_prior_sptr ()

void	set_prior_sptr (const shared_ptr< GeneralisedPrior< TargetT >> &)
	Change the prior. More...

Succeeded	add_multiplication_with_approximate_sub_Hessian_without_penalty (TargetT &output, const TargetT &input, const int subset_num) const
	Functions that multiply the approximate (sub)Hessian with a \'vector\'. More...

Succeeded	add_multiplication_with_approximate_sub_Hessian (TargetT &output, const TargetT &input, const int subset_num) const

Succeeded	add_multiplication_with_approximate_Hessian_without_penalty (TargetT &output, const TargetT &input) const

Succeeded	add_multiplication_with_approximate_Hessian (TargetT &output, const TargetT &input) const

Succeeded	accumulate_Hessian_times_input (TargetT &output, const TargetT &current_image_estimate, const TargetT &input) const
	Functions that multiply the True (sub)Hessian with a \'vector\'. More...

Succeeded	accumulate_Hessian_times_input_without_penalty (TargetT &output, const TargetT &current_image_estimate, const TargetT &input) const

Succeeded	accumulate_sub_Hessian_times_input (TargetT &output, const TargetT &current_image_estimate, const TargetT &input, const int subset_num) const

Succeeded	accumulate_sub_Hessian_times_input_without_penalty (TargetT &output, const TargetT &current_image_estimate, const TargetT &input, const int subset_num) const

Public Member Functions inherited from stir::ParsingObject
	ParsingObject (const ParsingObject &)

ParsingObject &	operator= (const ParsingObject &)

void	ask_parameters ()

bool	parse (std::istream &f)

bool	parse (const char *const filename)

Static Public Attributes
static const char *const	registered_name
	Name which will be used when parsing a GeneralisedObjectiveFunction object.

Protected Member Functions
double	actual_compute_objective_function_without_penalty (const TargetT &current_estimate, const int subset_num) override
	Implementation of function that computes the objective function for the current subset. More...

Succeeded	set_up_before_sensitivity (shared_ptr< const TargetT > const &target_sptr) override
	set-up specifics for the derived class

void	add_subset_sensitivity (TargetT &sensitivity, const int subset_num) const override
	Add subset sensitivity to existing data. More...

Succeeded	actual_add_multiplication_with_approximate_sub_Hessian_without_penalty (TargetT &output, const TargetT &input, const int subset_num) const override
	Implementation of the function that multiplies the approximate sub-Hessian with a vector. More...

Succeeded	actual_accumulate_sub_Hessian_times_input_without_penalty (TargetT &output, const TargetT &current_image_estimate, const TargetT &input, const int subset_num) const override
	Implementation of the function computes the sub-Hessian and multiplies by a vector. More...

bool	actual_subsets_are_approximately_balanced (std::string &warning_message) const override
	Implementation of function that checks subset balancing. More...

void	actual_compute_subset_gradient_without_penalty (TargetT &gradient, const TargetT &current_estimate, const int subset_num, const bool add_sensitivity) override
	computes the subset gradient of the objective function without the penalty (optional: add subset sensitivity) More...

void	set_defaults () override
	Sets defaults for parsing. More...

void	initialise_keymap () override
	sets parsing keys More...

bool	post_processing () override
	This will be called at the end of the parsing. More...

Protected Member Functions inherited from stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >
void	compute_sensitivities ()
	compute subset and total sensitivity More...

void	set_defaults () override
	Sets defaults for parsing. More...

void	initialise_keymap () override
	sets parsing keys More...

bool	post_processing () override
	This will be called at the end of the parsing. More...

Protected Member Functions inherited from stir::ParsingObject
virtual void	set_key_values ()
	This will be called before parsing or parameter_info is called. More...

Protected Attributes
std::string	_input_filename
	Filename with input projection data.

shared_ptr< DynamicProjData >	_dyn_proj_data_sptr
	points to the object for the total input projection data

int	_max_segment_num_to_process
	the maximum absolute ring difference number to use in the reconstruction More...

ParseAndCreateFrom< TargetT, DynamicProjData >	target_parameter_parser

std::string	_additive_dyn_proj_data_filename
	name of file in which additive projection data are stored

shared_ptr< DynamicProjData >	_additive_dyn_proj_data_sptr
	points to the additive projection data More...

shared_ptr< BinNormalisation >	_normalisation_sptr

shared_ptr< ProjectorByBinPair >	_projector_pair_ptr
	Stores the projectors that are used for the computations.

bool	_zero_seg0_end_planes
	signals whether to zero the data in the end planes of the projection data

shared_ptr< PatlakPlot >	_patlak_plot_sptr

DynamicDiscretisedDensity	_dyn_image_template
	dynamic image template

Protected Attributes inherited from stir::GeneralisedObjectiveFunction< TargetT >
int	num_subsets

bool	already_set_up

shared_ptr< GeneralisedPrior< TargetT > >	prior_sptr

Protected Attributes inherited from stir::ParsingObject
KeyParser	parser

Additional Inherited Members
Static Public Member Functions inherited from stir::RegisteredParsingObject< PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >, GeneralisedObjectiveFunction< TargetT >, PoissonLogLikelihoodWithLinearModelForMean< TargetT > >
static GeneralisedObjectiveFunction< TargetT > *	read_from_stream (std::istream *)
	Construct a new object (of type Derived) by parsing the istream. More...

Static Public Member Functions inherited from stir::RegisteredObject< GeneralisedObjectiveFunction< TargetT > >
static GeneralisedObjectiveFunction< TargetT > *	read_registered_object (std::istream *in, const std::string &registered_name)
	Construct a new object (of a type derived from Root, its actual type determined by the registered_name parameter) by parsing the istream. More...

static GeneralisedObjectiveFunction< TargetT > *	ask_type_and_parameters ()
	ask the user for the type, and then calls read_registered_object(0, type) More...

static void	list_registered_names (std::ostream &stream)
	List all possible registered names to the stream. More...

Protected Types inherited from stir::RegisteredObject< GeneralisedObjectiveFunction< TargetT > >
typedef GeneralisedObjectiveFunction< TargetT > (	RootFactory) (std::istream *)
	The type of a root factory is a function, taking an istream* as argument, and returning a Root*.

typedef FactoryRegistry< std::string, RootFactory, interfile_less >	RegistryType
	The type of the registry.

Static Protected Member Functions inherited from stir::RegisteredObject< GeneralisedObjectiveFunction< TargetT > >
static RegistryType &	registry ()
	Static function returning the registry. More...

Detailed Description

template<typename TargetT>
class stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >

a base class for LogLikelihood of independent Poisson variables where the mean values are linear combinations of the kinetic parameters.

Parameters for parsing

Member Function Documentation

◆ construct_target_ptr()

template<typename TargetT >

TargetT* stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::construct_target_ptr ( ) const

overridevirtual

Returns a pointer to a newly allocated target object (with 0 data).

Dimensions etc are set from the dyn_proj_data_sptr and other information set by parsing, such as zoom, output_image_size_z etc.

Implements stir::GeneralisedObjectiveFunction< TargetT >.

◆ actual_compute_objective_function_without_penalty()

template<typename TargetT >

double stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::actual_compute_objective_function_without_penalty	(	const TargetT &	current_estimate,
		const int	subset_num
	)

overrideprotectedvirtual

Implementation of function that computes the objective function for the current subset.

See also: compute_objective_function_without_penalty(const Target&,const int)

Developer\'s note

The reason we have this function is that overloading a function in a derived class, hides all functions of the same name.

Implements stir::GeneralisedObjectiveFunction< TargetT >.

◆ add_subset_sensitivity()

template<typename TargetT >

void stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::add_subset_sensitivity	(	TargetT &	sensitivity,
		const int	subset_num
	)		const

overrideprotectedvirtual

Add subset sensitivity to existing data.

Todo:: Current implementation does NOT add to the subset sensitivity, but overwrites

Implements stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

◆ actual_add_multiplication_with_approximate_sub_Hessian_without_penalty()

template<typename TargetT >

Succeeded stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::actual_add_multiplication_with_approximate_sub_Hessian_without_penalty	(	TargetT &	output,
		const TargetT &	input,
		const int	subset_num
	)		const

overrideprotectedvirtual

Implementation of the function that multiplies the approximate sub-Hessian with a vector.

See also: multiplication_with_approximate_sub_Hessian_without_penalty(TargetT&,const TargetT&, const int).

Warning: The default implementation just calls error(). This behaviour has to be overloaded by the derived classes.

Developer\'s note

The reason we have this function is that overloading a function in a derived class, hides all functions of the same name.

Reimplemented from stir::GeneralisedObjectiveFunction< TargetT >.

◆ actual_accumulate_sub_Hessian_times_input_without_penalty()

template<typename TargetT >

Succeeded stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::actual_accumulate_sub_Hessian_times_input_without_penalty	(	TargetT &	output,
		const TargetT &	current_image_estimate,
		const TargetT &	input,
		const int	subset_num
	)		const

overrideprotectedvirtual

Implementation of the function computes the sub-Hessian and multiplies by a vector.

See also: accumulate_sub_Hessian_times_input_without_penalty(TargetT&,const TargetT&, TargetT&, const int).

Warning: The default implementation just calls error(). This behaviour has to be overloaded by the derived classes.

Developer\'s note

The reason we have this function is that overloading a function in a derived class, hides all functions of the same name.

Reimplemented from stir::GeneralisedObjectiveFunction< TargetT >.

◆ set_num_subsets()

template<typename TargetT >

int stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::set_num_subsets ( const int num_subsets )

overridevirtual

Attempts to change the number of subsets.

Returns: The number of subsets that will be used later, which is not guaranteed to be what you asked for.

Implements stir::GeneralisedObjectiveFunction< TargetT >.

◆ set_normalisation_sptr()

template<typename TargetT >

void stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::set_normalisation_sptr ( const shared_ptr< BinNormalisation > & )

overridevirtual

set_normalisation_sptr

Author: Nikos Efthimiou

In the case the reconstruction process is called from another piece of code, the user should be able to set any additive sinogram

Implements stir::GeneralisedObjectiveFunction< TargetT >.

◆ set_additive_proj_data_sptr()

template<typename TargetT >

void stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::set_additive_proj_data_sptr ( const shared_ptr< ExamData > & )

overridevirtual

set_additive_proj_data_sptr

Author: Nikos Efthimiou

In the case the reconstruction process is called from another piece of code, the user should be able to set any additive sinogram

Implements stir::GeneralisedObjectiveFunction< TargetT >.

◆ set_input_data()

template<typename TargetT >

void stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::set_input_data ( const shared_ptr< ExamData > & )

overridevirtual

set_input_data

Author: Nikos Efthimiou

It can be used to set the data to be reconstructed within some other code, as opposed to via parsing.

Implements stir::GeneralisedObjectiveFunction< TargetT >.

◆ get_input_data()

template<typename TargetT >

const DynamicProjData& stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::get_input_data ( ) const

overridevirtual

get input data

Will throw an exception if it wasn't set first

Implements stir::GeneralisedObjectiveFunction< TargetT >.

◆ actual_subsets_are_approximately_balanced()

template<typename TargetT >

bool stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::actual_subsets_are_approximately_balanced ( std::string & warning_message ) const

overrideprotectedvirtual

Implementation of function that checks subset balancing.

See also: subsets_are_approximately_balanced(std::string&)

Developer\'s note

The reason we have this function is that overloading subsets_are_approximately_balanced(std::string&) in a derived class would hide subsets_are_approximately_balanced().

Implements stir::GeneralisedObjectiveFunction< TargetT >.

◆ actual_compute_subset_gradient_without_penalty()

template<typename TargetT >

void stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::actual_compute_subset_gradient_without_penalty	(	TargetT &	gradient,
		const TargetT &	current_estimate,
		const int	subset_num,
		const bool	add_sensitivity
	)

overrideprotectedvirtual

computes the subset gradient of the objective function without the penalty (optional: add subset sensitivity)

If add_sensitivity is true, this computes

${\partial L \over \partial \lambda_v} + P_v = \sum_b P_{bv} {y_b \over Y_b}$

(see the class general documentation). The sum will however be restricted to a subset.

However, if add_sensitivity is false, this function will instead compute only the gradient

${\partial L \over \partial \lambda_v} = \sum_b P_{bv} ({y_b \over Y_b} - 1)$

Implements stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

◆ set_defaults()

template<typename TargetT >

void stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::set_defaults ( )

overrideprotectedvirtual

Sets defaults for parsing.

Resets sensitivity_filename and sensitivity_sptr and recompute_sensitivity to false.

Reimplemented from stir::GeneralisedObjectiveFunction< TargetT >.

◆ initialise_keymap()

template<typename TargetT >

void stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::initialise_keymap ( )

overrideprotectedvirtual

sets parsing keys

Has to be called by initialise_keymap in the leaf-class

Reimplemented from stir::GeneralisedObjectiveFunction< TargetT >.

◆ post_processing()

template<typename TargetT >

bool stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::post_processing ( )

overrideprotectedvirtual

This will be called at the end of the parsing.

Returns: false if everything OK, true if not

Reimplemented from stir::ParsingObject.

Member Data Documentation

◆ _max_segment_num_to_process

template<typename TargetT >

int stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::_max_segment_num_to_process

protected

the maximum absolute ring difference number to use in the reconstruction

convention: if -1, use get_max_segment_num()

◆ _additive_dyn_proj_data_sptr

template<typename TargetT >

shared_ptr<DynamicProjData> stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::_additive_dyn_proj_data_sptr

protected

points to the additive projection data

the projection data in this file is bin-wise added to forward projection results

◆ _normalisation_sptr

template<typename TargetT >

shared_ptr<BinNormalisation> stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::_normalisation_sptr

protected

the normalisation or/and attenuation data

◆ _patlak_plot_sptr

template<typename TargetT >

shared_ptr<PatlakPlot> stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >::_patlak_plot_sptr

protected

the patlak plot pointer where all the parameters are stored

The documentation for this class was generated from the following file:

/home/sirfuser/devel/STIRdistrib/STIR/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData.h

Public Member Functions

Static Public Attributes

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

template<typename TargetT> class stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >

Member Function Documentation

◆ construct_target_ptr()

◆ actual_compute_objective_function_without_penalty()

◆ add_subset_sensitivity()

◆ actual_add_multiplication_with_approximate_sub_Hessian_without_penalty()

◆ actual_accumulate_sub_Hessian_times_input_without_penalty()

◆ set_num_subsets()

◆ set_normalisation_sptr()

◆ set_additive_proj_data_sptr()

◆ set_input_data()

◆ get_input_data()

◆ actual_subsets_are_approximately_balanced()

◆ actual_compute_subset_gradient_without_penalty()

◆ set_defaults()

◆ initialise_keymap()

◆ post_processing()

Member Data Documentation

◆ _max_segment_num_to_process

◆ _additive_dyn_proj_data_sptr

◆ _normalisation_sptr

◆ _patlak_plot_sptr

template<typename TargetT>
class stir::PoissonLogLikelihoodWithLinearKineticModelAndDynamicProjectionData< TargetT >