Class for PET list mode data from static images for a scanner with discrete detectors. More...

#include "stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin.h"

Inheritance diagram for stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >:

[legend]

Public Member Functions
double	actual_compute_objective_function_without_penalty (const TargetT &current_estimate, const int subset_num) override
	Computes the value of the objective function at the current_estimate. 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 gradient of the objective function at the current_estimate overwriting gradient. 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...

TargetT *	construct_target_ptr () const override
	Creates a suitable target as determined by the parameters. More...

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

const shared_ptr< BinNormalisation > &	get_normalisation_sptr () const

unique_ptr< ExamInfo >	get_exam_info_uptr_for_target () const override

void	set_proj_matrix (const shared_ptr< ProjMatrixByBin > &)

void	set_skip_balanced_subsets (const bool arg)

STIR_DEPRECATED void	set_max_ring_difference (const int arg)

Public Member Functions inherited from stir::RegisteredParsingObject< PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >, GeneralisedObjectiveFunction< TargetT >, PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< 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::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >
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 ListModeData &	get_input_data () const override
	get input data More...

void	set_max_segment_num_to_process (int)
	set maximum segment_number (in listmode data) to process More...

int	get_max_segment_num_to_process () const
	get maximum segment_number (from listmode data) to process More...

virtual void	set_cache_path (const std::string &cache_path)
	Set the directory where data will be cached. More...

virtual std::string	get_cache_path () const
	Get the directory where data will be cached.

virtual std::string	get_cache_filename (unsigned int icache) const
	Get the filename for a cache file. More...

void	set_recompute_cache (bool)
	Set if existing cache files should be used or not. More...

bool	get_recompute_cache () const

void	set_skip_lm_input_file (const bool arg)
	Skip reading of listmode file entirely, only read from cache (broken!) More...

virtual void	set_cache_max_size (const unsigned long int arg)
	Set maximum size (in bytes) of cache in memory. More...

virtual unsigned long int	get_cache_max_size () const
	Get maximum size (in bytes) of cache.

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 = "PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin"
	Name which will be used when parsing a GeneralisedObjectiveFunction object.

Protected Member Functions
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.

void	set_defaults () override
	sets any default values

void	initialise_keymap () override
	sets keys for parsing

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

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

Protected Member Functions inherited from stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >
virtual void	start_new_time_frame (const unsigned int new_frame_num)
	will be called when a new time frame starts More...

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

Protected Member Functions inherited from stir::GeneralisedObjectiveFunction< TargetT >
virtual Succeeded	actual_add_multiplication_with_approximate_sub_Hessian_without_penalty (TargetT &output, const TargetT &input, const int subset_num) const
	Implementation of the function that multiplies the approximate sub-Hessian with a vector. 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
int	max_ring_difference_num_to_process
	Maximum ring difference to take into account. More...

bool	use_tofsens
	Triggers calculation of sensitivity using time-of-flight.

shared_ptr< ProjMatrixByBin >	PM_sptr
	Stores the projectors that are used for the computations.

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

shared_ptr< BackProjectorByBin >	sens_backprojector_sptr
	Backprojector used for sensitivity computation.

shared_ptr< ProjDataInfo >	sens_proj_data_info_sptr
	Proj data info to be used for sensitivity calculations. More...

bool	skip_balanced_subsets
	If you know, or have previously checked that the number of subsets is balanced for your Scanner geometry, you can skip future checks.

Protected Attributes inherited from stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >
std::string	frame_defs_filename

std::string	list_mode_filename
	Filename with input projection data.

shared_ptr< ProjData >	additive_proj_data_sptr

std::string	additive_projection_data_filename
	filename for additive data (only used when parsing)

bool	reduce_memory_usage
	If `true`, the additive sinogram will not be read in memory.

shared_ptr< BinNormalisation >	normalisation_sptr

shared_ptr< ListModeData >	list_mode_data_sptr
	Listmode pointer.

unsigned int	current_frame_num

long int	num_events_to_use
	This is part of some functionality I transfer from LmToProjData.

bool	do_time_frame
	Reconstruct based on time frames.

ParseAndCreateFrom< TargetT, ListModeData >	target_parameter_parser

unsigned long int	cache_size
	This is the number of records to be cached. If this parameter is more than zero, then the flag cache_lm_file will be set to true. The listmode file up to this size will be loaded in the RAM, alongside with any additive sinograms.

bool	cache_lm_file
	This flag is true when cache_size is more than zero.

bool	recompute_cache
	On the first cached run, the cache will be written in the cache_path. If recompute_cache is set to zero then every consecutive reconstruction will use that cache file. If you want to create a new, either delete the previous or set this 1. More...

bool	skip_lm_input_file
	This flag is set when we don't set an input lm filename and rely only on the cache file.

std::string	cache_path
	Path to read/write the cached listmode file. More...

bool	has_add
	The data set has additive corrections.

shared_ptr< ProjDataInfo >	proj_data_info_sptr
	ProjDataInfo. More...

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< PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >, GeneralisedObjectiveFunction< TargetT >, PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< 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...

Public Attributes inherited from stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >
TimeFrameDefinitions	frame_defs
	time frame definitions 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::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >

Class for PET list mode data from static images for a scanner with discrete detectors.

If the scanner has discrete (and stationary) detectors, it can be modeled via ProjMatrixByBin and BinNormalisation.

See also: PoissonLogLikelihoodWithLinearModelForMeanAndProjData

If the list mode data is binned (with LmToProjData) without merging any bins, then the log likelihood computed from list mode data and projection data will be identical.

Currently, the subset scheme is the same for the projection data and listmode data, i.e. based on views. This is suboptimal for listmode data.

Todo:: implement a subset scheme based on events

Member Function Documentation

◆ actual_compute_objective_function_without_penalty()

template<typename TargetT >

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

overridevirtual

Computes the value of the objective function at the current_estimate.

Warning: If add_sensitivity = false and use_subset_sensitivities = false will return an error because the value will not be correct. Try use_subset_sensitivities = true.

Implements stir::GeneralisedObjectiveFunction< TargetT >.

References stir::error(), stir::inner_product(), and stir::LM_distributable_computation().

◆ actual_compute_subset_gradient_without_penalty()

template<typename TargetT >

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

overridevirtual

Computes the gradient of the objective function at the current_estimate overwriting gradient.

Warning: If add_sensitivity = false and use_subset_sensitivities = false will return an error because the gradient will not be correct. Try use_subset_sensitivities = true.

Implements stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

References stir::error().

◆ actual_accumulate_sub_Hessian_times_input_without_penalty()

template<typename TargetT >

Succeeded stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< 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 >.

References stir::error().

◆ construct_target_ptr()

template<typename TargetT >

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

overridevirtual

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 >.

◆ set_num_subsets()

template<typename TargetT >

int stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< 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 >.

References stir::ExamData::get_exam_info_sptr(), and stir::ProjData::get_proj_data_info_sptr().

Referenced by stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBinTests::run_tests_for_objective_function().

◆ post_processing()

template<typename TargetT >

bool stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< 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::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >.

References stir::error(), stir::FilePath::get_as_string(), stir::info(), stir::FilePath::is_regular_file(), and stir::warning().

◆ actual_subsets_are_approximately_balanced()

template<typename TargetT >

bool stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< 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 stir::Array< num_dimensions, elemT >::fill(), stir::DataSymmetriesForBins::num_related_bins(), and stir::warning().