An objective function class appropriate for PET list mode data. More...

#include "stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeData.h"

Inheritance diagram for stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >:

[legend]

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

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 ListModeData &	get_input_data () const override
	get input data

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

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

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.

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.

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.

virtual void	add_subset_sensitivity (TargetT &sensitivity, const int subset_num) const =0
	Add subset sensitivity to existing data.

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 TargetT *	construct_target_ptr () const =0
	Creates a suitable target as determined by the parameters.

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

virtual int	set_num_subsets (const int num_subsets)=0
	Attempts to change the number of subsets.

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::RegisteredObjectBase
virtual std::string	get_registered_name () const =0
	Returns the name of the type of the object.

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 ()

virtual std::string	parameter_info ()

Public Attributes
TimeFrameDefinitions	frame_defs
	time frame definitions

caching-related methods
These functions can be used to cache listmode events into memory, allowing parallelised processing. Currently, the cached data is written to one or more files ( See also get_cache_filename) Warning This code is experimental and likely to change in future versions. When re-using an existing cache, there is no check if time-frames etc are the same as what was used when creating the cache. This is therefore quite risky. Cache-files are written in a binary format that likely depends on compiler, endianness etc. Todo It should be possible to read only part of the cache in memory.
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.

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.

bool	has_add
	The data set has additive corrections.

shared_ptr< ProjDataInfo >	proj_data_info_sptr
	ProjDataInfo.

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

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.

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

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!)

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

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

void	set_defaults () override
	sets any default values

void	initialise_keymap () override
	sets keys

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

virtual void	start_new_time_frame (const unsigned int new_frame_num)
	will be called when a new time frame starts

Additional Inherited Members
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.

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.

void	compute_sensitivities ()
	compute subset and total sensitivity

virtual void	actual_compute_subset_gradient_without_penalty (TargetT &gradient, const TargetT &current_estimate, const int subset_num, const bool add_sensitivity)=0
	computes the subset gradient of the objective function without the penalty (optional: add subset sensitivity)

virtual bool	actual_subsets_are_approximately_balanced (std::string &warning_message) const =0
	Implementation of function that checks subset balancing.

virtual double	actual_compute_objective_function_without_penalty (const TargetT &current_estimate, const int subset_num)=0
	Implementation of function that computes the objective function for the current subset.

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.

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

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

Detailed Description

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

An objective function class appropriate for PET list mode data.

The statistics for list mode data is slightly different from having a set of counts, see the paper H. H. Barrett, L. Parra, and T. White, List-mode likelihood, J. Optical Soc. Amer. A, vol. 14, no. 11, 1997.

However, it is intuitive that the list mode likelihood can be derived from that 'binned' likelihood by taking smaller and smaller time bins. One can then see that the gradient of the list mode likelihood can be computed similar to the 'binned' case, but now with a sum over events. The sensitivity still needs a sum (or integral) over all possible detections.

At present, STIR does not contain any classes to forward/back project list mode data explictly. So, the sum over events cannot be computed for arbitrary list mode data. This is currently done in derived classes.

For list mode reconstructions, computing the sensitivity is sometimes conceptually very difficult (how to compute the integral) or impractical. So, even if we will be able to compute the sum over events in a generic way, the add_subset_sensitivity() function will have to be implemented by a derived class, specific for the measurement.

Member Function Documentation

◆ set_up_before_sensitivity()

template<typename TargetT>

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

overridevirtual

set-up specifics for the derived class

Implements stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

Reimplemented in stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >.

Referenced by set_up_before_sensitivity().

◆ set_normalisation_sptr()

template<typename TargetT>

void stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< 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 >.

References set_normalisation_sptr().

Referenced by set_normalisation_sptr().

◆ set_additive_proj_data_sptr()

template<typename TargetT>

void stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< 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 >.

References stir::error(), has_add, reduce_memory_usage, and set_additive_proj_data_sptr().

Referenced by set_additive_proj_data_sptr().

◆ set_input_data()

template<typename TargetT>

void stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< 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 >.

References list_mode_data_sptr, and set_input_data().

Referenced by set_input_data().

◆ get_input_data()

template<typename TargetT>

const ListModeData & stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >::get_input_data ( ) const

overridevirtual

get input data

Will throw an exception if it wasn't set first

Implements stir::GeneralisedObjectiveFunction< TargetT >.

References stir::error(), get_input_data(), and list_mode_data_sptr.

Referenced by stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >::construct_target_ptr(), and get_input_data().

◆ set_max_segment_num_to_process()

template<typename TargetT>

void stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >::set_max_segment_num_to_process ( int arg )

set maximum segment_number (in listmode data) to process

minimum will be -max_segment_num_to_process

Use -1 to process all.

References set_max_segment_num_to_process().

Referenced by set_max_segment_num_to_process().

◆ get_max_segment_num_to_process()

template<typename TargetT>

int stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >::get_max_segment_num_to_process ( ) const

get maximum segment_number (from listmode data) to process

See also: set_max_segment_num_to_process

References get_max_segment_num_to_process().

Referenced by get_max_segment_num_to_process(), and stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >::post_processing().

◆ set_cache_path()

template<typename TargetT>

void stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >::set_cache_path ( const std::string & cache_path )

virtual

Set the directory where data will be cached.

Parameters

cache_path directory-name (defaults to current directory). The directory has to exist.

References cache_path, and set_cache_path().

Referenced by set_cache_path().

◆ get_cache_filename()

template<typename TargetT>

std::string stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >::get_cache_filename ( unsigned int icache ) const

virtual

Get the filename for a cache file.

Filenames are currently "my_CACHE%d.bin" with get_cache_path() prepended.

References stir::FilePath::get_as_string(), get_cache_filename(), get_cache_path(), and stir::FilePath::prepend_directory_name().

Referenced by get_cache_filename().

◆ set_recompute_cache()

template<typename TargetT>

void stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >::set_recompute_cache ( bool v )

Set if existing cache files should be used or not.

defaults to true

References recompute_cache, and set_recompute_cache().

Referenced by set_recompute_cache().

◆ set_skip_lm_input_file()

template<typename TargetT>

void stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >::set_skip_lm_input_file ( const bool arg )

Skip reading of listmode file entirely, only read from cache (broken!)

Warning: This currently aborts, as functionality is broken. (We would need to be able to read proj_data_info and exam_info).

Todo: replace with reading from a custom-listmode file (although this would have to support the additive term).

Todo: in the future the following statements should be removed.

References cache_path, stir::error(), set_skip_lm_input_file(), stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >::set_subsensitivity_filenames(), stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >::set_use_subset_sensitivities(), and skip_lm_input_file.

Referenced by set_skip_lm_input_file().

◆ set_cache_max_size()

template<typename TargetT>

void stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >::set_cache_max_size ( const unsigned long int arg )

virtual

Set maximum size (in bytes) of cache in memory.

When all events do not fit in the cache, several cache files will be used.

As multi-threading occurs over cached events, it is likely that better performance will be obtained with a large cache size.

References cache_size, and set_cache_max_size().

Referenced by set_cache_max_size().

◆ set_defaults()

template<typename TargetT>

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

overrideprotectedvirtual

sets any default values

Has to be called by set_defaults in the leaf-class

Reimplemented from stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

Reimplemented in stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >, and stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< stir::DiscretisedDensity< 3, float > >.

References additive_projection_data_filename, cache_lm_file, cache_path, cache_size, frame_defs, has_add, list_mode_data_sptr, list_mode_filename, num_events_to_use, recompute_cache, reduce_memory_usage, stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >::set_defaults(), set_defaults(), and skip_lm_input_file.

Referenced by set_defaults().

◆ initialise_keymap()

template<typename TargetT>

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

overrideprotectedvirtual

sets keys

Has to be called by initialise_keymap in the leaf-class

Reimplemented from stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

Reimplemented in stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >, and stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< stir::DiscretisedDensity< 3, float > >.

References additive_projection_data_filename, stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >::initialise_keymap(), initialise_keymap(), list_mode_filename, and reduce_memory_usage.

Referenced by initialise_keymap().

◆ post_processing()

template<typename TargetT>

bool stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< 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 >.

Reimplemented in stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >, and stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< stir::DiscretisedDensity< 3, float > >.

References stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >::post_processing(), and post_processing().

Referenced by post_processing().

◆ start_new_time_frame()

template<typename TargetT>

void stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >::start_new_time_frame ( const unsigned int new_frame_num )

protectedvirtual

will be called when a new time frame starts

The frame numbers start from 1.

References start_new_time_frame().

Referenced by start_new_time_frame().

Member Data Documentation

◆ frame_defs

template<typename TargetT>

TimeFrameDefinitions stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >::frame_defs

time frame definitions

Todo: This is currently used to be able to compute the gradient for one time frame. However, it probably does not belong here. For instance when fitting kinetic model parameters from list mode data, time frames are in principle irrelevant. So, we will probably shift this to the derived class.

Referenced by set_defaults(), set_up_before_sensitivity(), and stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >::set_up_before_sensitivity().

◆ recompute_cache

template<typename TargetT>

bool stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >::recompute_cache

protected

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.

Todo: multiple cache files need to be supported!

Referenced by set_defaults(), and set_recompute_cache().

◆ cache_path

template<typename TargetT>

std::string stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >::cache_path

protected

Path to read/write the cached listmode file.

Todo: add the ability to set a filename.

Referenced by get_cache_path(), set_cache_path(), set_defaults(), and set_skip_lm_input_file().

◆ proj_data_info_sptr

template<typename TargetT>

shared_ptr<ProjDataInfo> stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeData< TargetT >::proj_data_info_sptr

protected

ProjDataInfo.

normally a copy of the one from the listmode file, but could be reduced in size

Referenced by stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >::actual_subsets_are_approximately_balanced(), stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >::add_subset_sensitivity(), stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >::post_processing(), set_up_before_sensitivity(), and stir::PoissonLogLikelihoodWithLinearModelForMeanAndListModeDataWithProjMatrixByBin< TargetT >::set_up_before_sensitivity().

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

/home/kris/devel/STIRdistrib/STIR/src/include/stir/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeData.h
/home/kris/devel/STIRdistrib/STIR/src/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndListModeData.cxx

Public Member Functions

Public Attributes

caching-related methods

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ set_up_before_sensitivity()

◆ set_normalisation_sptr()

◆ set_additive_proj_data_sptr()

◆ set_input_data()

◆ get_input_data()

◆ set_max_segment_num_to_process()

◆ get_max_segment_num_to_process()

◆ set_cache_path()

◆ get_cache_filename()

◆ set_recompute_cache()

◆ set_skip_lm_input_file()

◆ set_cache_max_size()

◆ set_defaults()

◆ initialise_keymap()

◆ post_processing()

◆ start_new_time_frame()

Member Data Documentation

◆ frame_defs

◆ recompute_cache

◆ cache_path

◆ proj_data_info_sptr