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

#include "stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h"

Inheritance diagram for stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >:

[legend]

Public Member Functions
TargetT *	construct_target_ptr () const override
	Returns a pointer to a newly allocated target object (with 0 data).
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)
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.
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.
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.
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.
void	set_time_gate_definitions (const TimeGateDefinitions &time_gate_definitions)
	PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion ()
	Default constructor calls set_defaults().
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 GatedProjData &	get_gated_proj_data () const
const shared_ptr< GatedProjData > &	get_gated_proj_data_sptr () const
const int	get_max_segment_num_to_process () const
const bool	get_zero_seg0_end_planes () const
const GatedProjData &	get_additive_gated_proj_data () const
const shared_ptr< GatedProjData > &	get_additive_gated_proj_data_sptr () const
const GatedProjData &	get_normalisation_gated_proj_data () const
const shared_ptr< GatedProjData > &	get_normalisation_gated_proj_data_sptr () const
const ProjectorByBinPair &	get_projector_pair () const
const shared_ptr< ProjectorByBinPair > &	get_projector_pair_sptr () const
Public Member Functions inherited from stir::RegisteredParsingObject< PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< 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.
Succeeded	set_up (shared_ptr< TargetT > const &target_sptr) override
	set-up sensitivity etc if possible
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
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
std::string	get_subsensitivity_filenames () const
	get filename pattern to read (or write) the subset sensitivities
std::string	get_subsensitivity_filename (const int subset_num) const
	Return the filename for a particular subset.
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
void	set_subsensitivity_filenames (const std::string &)
	set filename pattern to read (or write) the subset sensitivities
void	fill_nonidentifiable_target_parameters (TargetT &target, const float value) const override
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.
virtual void	compute_gradient (TargetT &gradient, const TargetT &current_estimate)
	Compute the gradient of the objective function at the current_estimate.
virtual void	compute_gradient_without_penalty (TargetT &gradient, const TargetT &current_estimate)
	Compute the gradient of the unregularised objective function at the current_estimate.
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.
virtual double	compute_objective_function_without_penalty (const TargetT &current_estimate)
	Compute the value of the unregularised objective function at the current_estimate.
double	compute_penalty (const TargetT &current_estimate, const int subset_num)
	Compute the value of the sub-penalty at the current_estimate.
double	compute_penalty (const TargetT &current_estimate)
	Compute the value of the penalty at the current_estimate.
double	compute_objective_function (const TargetT &current_estimate, const int subset_num)
	Compute the value of the sub-objective function at the current_estimate.
double	compute_objective_function (const TargetT &current_estimate)
	Compute the value of the objective function at the current_estimate.
double	compute_value (const TargetT &current_estimate)
	Alias for compute_objective_function(const TargetT&).
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\'.
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\'.
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
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.
virtual std::unique_ptr< ExamInfo >	get_exam_info_uptr_for_target () const
bool	subsets_are_approximately_balanced () const
	Checks of the current subset scheme is approximately balanced.
bool	subsets_are_approximately_balanced (std::string &warning_message) const
	Checks of the current subset scheme is approximately balanced and constructs a warning message.
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.
shared_ptr< GeneralisedPrior< TargetT > >	get_prior_sptr ()
void	set_prior_sptr (const shared_ptr< GeneralisedPrior< TargetT > > &)
	Change the prior.
Public Member Functions inherited from stir::ParsingObject
	ParsingObject (const ParsingObject &)
ParsingObject &	operator= (const ParsingObject &)
bool	parse (std::istream &f)
bool	parse (const char *const filename)
void	ask_parameters ()
Public Member Functions inherited from stir::RegisteredParsingObject< PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >, GeneralisedObjectiveFunction< TargetT >, SumOfGeneralisedObjectiveFunctions< PoissonLogLikelihoodWithLinearModelForMeanAndProjData< TargetT >, 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::SumOfGeneralisedObjectiveFunctions< ObjFuncT, TargetT, ParentT >
template<typename IterT>
	SumOfGeneralisedObjectiveFunctions (IterT begin, IterT end)
template<typename IterT>
void	set_functions (IterT begin, IterT end)
virtual Succeeded	set_up (shared_ptr< TargetT > const &target_sptr)
	Has to be called before using this object.
virtual void	compute_sub_gradient_without_penalty (TargetT &gradient, const TargetT &current_estimate, const int subset_num)
	This computes the gradient of the unregularised objective function at the current_estimate.

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

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.
std::string	_input_filename
	Filename with input projection data.
std::string	_motion_vectors_filename_prefix
std::string	_reverse_motion_vectors_filename_prefix
std::string	_gate_definitions_filename
shared_ptr< GatedProjData >	_gated_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
ParseAndCreateFrom< TargetT, GatedProjData >	target_parameter_parser
std::string	_additive_gated_proj_data_filename
	name of file in which additive projection data are stored
std::string	_normalisation_gated_proj_data_filename
	name of file in which normalisation projection data are stored
shared_ptr< GatedProjData >	_additive_gated_proj_data_sptr
	points to the additive projection data
shared_ptr< GatedProjData >	_normalisation_gated_proj_data_sptr
std::string	_normalisation_filename_prefix
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
int	_motion_correction_type
GatedSpatialTransformation	_motion_vectors
GatedSpatialTransformation	_reverse_motion_vectors
GatedDiscretisedDensity	_gated_image_template
	gated image template
int	max_segment_num_to_process
	the maximum absolute ring difference number to use in the reconstruction
int	output_image_size_xy
	the output image size in x and y direction
int	output_image_size_z
	the output image size in z direction
double	zoom
	the zoom factor
double	Xoffset
	offset in the x-direction
double	Yoffset
	offset in the y-direction
double	Zoffset
	offset in the z-direction
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
std::string	_additive_projection_data_filename
	name of file in which loglikelihood measurements are stored
int	frame_num
std::string	frame_definition_filename
TimeFrameDefinitions	frame_defs
VectorWithOffset< shared_ptr< BinNormalisation > >	_normalisation_sptrs
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.
void	set_normalisation_sptr (const shared_ptr< BinNormalisation > &) override
	set_normalisation_sptr
void	set_additive_proj_data_sptr (const shared_ptr< ExamData > &) override
	set_additive_proj_data_sptr
void	set_input_data (const shared_ptr< ExamData > &) override
	set_input_data
const GatedProjData &	get_input_data () const override
	get input data
void	set_proj_data_sptr (const shared_ptr< GatedProjData > &)
void	set_max_segment_num_to_process (const int)
void	set_zero_seg0_end_planes (const bool)
void	set_additive_proj_data_sptr (const shared_ptr< GatedProjData > &)
void	set_projector_pair_sptr (const shared_ptr< ProjectorByBinPair > &)
void	set_frame_num (const int)
void	set_frame_definitions (const TimeFrameDefinitions &)
virtual TargetT *	construct_target_ptr () const
	Creates a suitable target as determined by the parameters.
virtual void	compute_sub_gradient_without_penalty_plus_sensitivity (TargetT &gradient, const TargetT &target, const int subset_num)
	This should compute the subset gradient of the (unregularised) objective function plus the subset sensitivity.
const TimeGateDefinitions &	get_time_gate_definitions () const
bool	actual_subsets_are_approximately_balanced (std::string &warning_message) const override
	Implementation of function that checks subset balancing.
void	set_defaults () override
	Sets defaults before parsing.
void	initialise_keymap () override
	sets parsing keys
bool	post_processing () override
	This will be called at the end of the parsing.
virtual Succeeded	set_up_before_sensitivity (shared_ptr< TargetT > const &target_sptr)
virtual void	add_subset_sensitivity (TargetT &sensitivity, const int subset_num) const
	Add subset sensitivity to existing data.

Additional Inherited Members
Static Public Member Functions inherited from stir::RegisteredParsingObject< PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >, GeneralisedObjectiveFunction< TargetT >, PoissonLogLikelihoodWithLinearModelForMean< TargetT > >
static GeneralisedObjectiveFunction< TargetT > *	read_from_stream (std::istream *)
	Construct a new object (of type Derived) by parsing the istream.
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.
static GeneralisedObjectiveFunction< TargetT > *	ask_type_and_parameters ()
	ask the user for the type, and then calls read_registered_object(0, type)
static void	list_registered_names (std::ostream &stream)
	List all possible registered names to the stream.
Static Public Member Functions inherited from stir::RegisteredParsingObject< PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >, GeneralisedObjectiveFunction< TargetT >, SumOfGeneralisedObjectiveFunctions< PoissonLogLikelihoodWithLinearModelForMeanAndProjData< TargetT >, TargetT, PoissonLogLikelihoodWithLinearModelForMean< TargetT > > >
static GeneralisedObjectiveFunction< TargetT > *	read_from_stream (std::istream *)
	Construct a new object (of type Derived) by parsing the istream.
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.
Protected Types inherited from stir::SumOfGeneralisedObjectiveFunctions< ObjFuncT, TargetT, ParentT >
typedef std::vector< ObjFuncT >	_functions_type
typedef _functions_type::iterator	_functions_iterator_type
typedef _functions_type::const_iterator	_functions_const_iterator_type
void	compute_sensitivities ()
	compute subset and total sensitivity
virtual void	set_key_values ()
	This will be called before parsing or parameter_info is called.
Static Protected Member Functions inherited from stir::RegisteredObject< GeneralisedObjectiveFunction< TargetT > >
static RegistryType &	registry ()
	Static function returning the registry.
int	num_subsets
bool	already_set_up
shared_ptr< GeneralisedPrior< TargetT > >	prior_sptr
KeyParser	parser
Protected Attributes inherited from stir::SumOfGeneralisedObjectiveFunctions< ObjFuncT, TargetT, ParentT >
_functions_type	_functions

Detailed Description

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

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

$\‍[ \begin{array}{lcl} \Lambda_{\nu}^{(s+1)}&&=\Lambda_{\nu}^{(s)} \frac{1}{ \sum\limits_{b\in S_{l}, g} \sum\limits_{\nu'} \hat{W}^{-1} _{\nu'g\rightarrow \nu}P_{\nu' b}A_{bg}+\beta \nabla_{\Lambda_{\nu}} E_{\nu}^{(s)}}\\ &&\times \sum\limits_{b\in S_{l}, g} \sum\limits_{\nu'}\left(\hat{W}^{-1} _{\nu'g\rightarrow \nu}P_{\nu' b}\frac{Y_{bg}}{\sum\limits_{\tilde{\nu}}P_{b\tilde{\nu}}\sum\limits_{\tilde{\nu}'}\hat{W} _{\tilde{\nu}'\rightarrow \tilde{\nu}g}\Lambda_{\tilde{\nu}'}^{(s)}+\frac{B_{bg}}{A_{bg}}}\right) \end{array} \‍]$

Parameters for parsing

For more information: Tsoumpas et al (2013) Physics in Medicine and Biology

Constructor & Destructor Documentation

◆ PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion()

template<typename TargetT>

stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion ( )

Default constructor calls set_defaults().

References set_additive_proj_data_sptr().

Member Function Documentation

◆ construct_target_ptr() [1/2]

template<typename TargetT>

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

overridevirtual

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

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

Implements stir::GeneralisedObjectiveFunction< TargetT >.

References _gated_proj_data_sptr, and construct_target_ptr().

Referenced by construct_target_ptr().

◆ actual_compute_subset_gradient_without_penalty()

template<typename TargetT>

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

overridevirtual

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 >.

◆ actual_compute_objective_function_without_penalty()

template<typename TargetT>

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

overridevirtual

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 >.

◆ set_up_before_sensitivity()

template<typename TargetT>

Succeeded stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >::set_up_before_sensitivity ( shared_ptr< const TargetT > const & target_sptr )

overridevirtual

set-up specifics for the derived class

Implements stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

References add_subset_sensitivity().

Referenced by construct_target_ptr().

◆ add_subset_sensitivity() [1/2]

template<typename TargetT>

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

overridevirtual

Add subset sensitivity to existing data.

Implements stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

References add_subset_sensitivity().

Referenced by add_subset_sensitivity(), and set_up_before_sensitivity().

◆ actual_add_multiplication_with_approximate_sub_Hessian_without_penalty()

template<typename TargetT>

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

overridevirtual

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::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >::actual_accumulate_sub_Hessian_times_input_without_penalty	(	TargetT &	output,
		const TargetT &	current_image_estimate,
		const TargetT &	input,
		const int	subset_num ) const

overridevirtual

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::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< 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::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< 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::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< 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 >.

Referenced by PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion().

◆ set_input_data()

template<typename TargetT>

void stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< 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 GatedProjData & stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< 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::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< 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 >.

References initialise_keymap(), post_processing(), and set_defaults().

◆ set_defaults()

template<typename TargetT>

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

overrideprotectedvirtual

Sets defaults before parsing.

Reimplemented from stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

References _additive_projection_data_filename, _gated_proj_data_sptr, _input_filename, max_segment_num_to_process, output_image_size_xy, output_image_size_z, projector_pair_ptr, stir::ParsingObject::set_defaults(), set_defaults(), Xoffset, Yoffset, zero_seg0_end_planes, Zoffset, and zoom.

Referenced by actual_subsets_are_approximately_balanced(), and set_defaults().

◆ initialise_keymap()

template<typename TargetT>

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

overrideprotectedvirtual

sets parsing keys

Has to be called by initialise_keymap in the leaf-class

Reimplemented from stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

References _additive_projection_data_filename, _input_filename, stir::ParsingObject::initialise_keymap(), initialise_keymap(), max_segment_num_to_process, output_image_size_xy, output_image_size_z, projector_pair_ptr, zero_seg0_end_planes, Zoffset, and zoom.

Referenced by actual_subsets_are_approximately_balanced(), and initialise_keymap().

◆ post_processing()

template<typename TargetT>

bool stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< 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::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

References _additive_projection_data_filename, _gated_proj_data_sptr, _input_filename, output_image_size_xy, output_image_size_z, stir::ParsingObject::post_processing(), post_processing(), stir::warning(), and zoom.

Referenced by actual_subsets_are_approximately_balanced(), and post_processing().

◆ construct_target_ptr() [2/2]

template<typename TargetT>

virtual TargetT * stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >::construct_target_ptr ( ) const

virtual

Creates a suitable target as determined by the parameters.

Warning: This should not check already_set_up (unfortunately), as it is currently called in Reconstruction::reconstruct() before calling set_up().

Implements stir::GeneralisedObjectiveFunction< TargetT >.

References compute_sub_gradient_without_penalty_plus_sensitivity(), and set_up_before_sensitivity().

◆ compute_sub_gradient_without_penalty_plus_sensitivity()

template<typename TargetT>

void stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >::compute_sub_gradient_without_penalty_plus_sensitivity	(	TargetT &	gradient,
		const TargetT &	current_estimate,
		const int	subset_num )

virtual

This should compute the subset gradient of the (unregularised) objective function plus the subset sensitivity.

Implementation in terms of actual_compute_sub_gradient_without_penalty(). This function is used for instance by OSMAPOSL.

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.

Reimplemented from stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

References compute_sub_gradient_without_penalty_plus_sensitivity().

Referenced by compute_sub_gradient_without_penalty_plus_sensitivity(), and construct_target_ptr().

◆ add_subset_sensitivity() [2/2]

template<typename TargetT>

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

int stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >::_motion_correction_type

protected

the motion vectors where all information is stored

◆ max_segment_num_to_process

template<typename TargetT>

int stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< 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()

Referenced by initialise_keymap(), and set_defaults().

◆ output_image_size_xy

template<typename TargetT>

int stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >::output_image_size_xy

protected

the output image size in x and y direction

convention: if -1, use a size such that the whole FOV is covered

Referenced by initialise_keymap(), post_processing(), and set_defaults().

◆ output_image_size_z

template<typename TargetT>

/home/kris/devel/STIRdistrib/STIR/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h
/home/kris/devel/STIRdistrib/STIR/src/include/stir_experimental/motion/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.h
/home/kris/devel/STIRdistrib/STIR/src/experimental/motion/PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion.cxx

Public Member Functions

Static Public Attributes

Functions to set parameters

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion()

Member Function Documentation

◆ construct_target_ptr() [1/2]

◆ actual_compute_subset_gradient_without_penalty()

◆ actual_compute_objective_function_without_penalty()

◆ set_up_before_sensitivity()

◆ add_subset_sensitivity() [1/2]

◆ 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()

◆ set_defaults()

◆ initialise_keymap()

◆ post_processing()

◆ construct_target_ptr() [2/2]

◆ compute_sub_gradient_without_penalty_plus_sensitivity()

◆ add_subset_sensitivity() [2/2]

Member Data Documentation

◆ _input_filename

◆ _gated_proj_data_sptr

◆ _max_segment_num_to_process

◆ _additive_gated_proj_data_sptr

◆ _normalisation_filename_prefix

◆ _motion_correction_type

◆ max_segment_num_to_process

◆ output_image_size_xy

◆ output_image_size_z

◆ zoom

◆ Xoffset

◆ Yoffset

◆ Zoffset

◆ projector_pair_ptr

◆ zero_seg0_end_planes

◆ _additive_projection_data_filename