STIR  6.2.0
Public Member Functions | Static Public Attributes | Protected Member Functions | Protected Attributes | List of all members
stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT > Class Template Reference

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 >:
Inheritance graph
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Public Member Functions

TargetT * construct_target_ptr () const override
 Returns a pointer to a newly allocated target object (with 0 data). 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...
 
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.
 
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...
 
void set_time_gate_definitions (const TimeGateDefinitions &time_gate_definitions)
 
 PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion ()
 Default constructor calls set_defaults()
 
virtual TargetT * construct_target_ptr () const
 Creates a suitable target as determined by the parameters. More...
 
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. More...
 
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 ProjectorByBinPairget_projector_pair () const
 
const shared_ptr< ProjectorByBinPair > & get_projector_pair_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 GatedProjData & get_input_data () const override
 get input data More...
 
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 &)
 
- 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< PoissonLogLikelihoodWithLinearModelForMeanAndProjData< TargetT >, TargetT, PoissonLogLikelihoodWithLinearModelForMean< TargetT > >
 SumOfGeneralisedObjectiveFunctions (IterT begin, IterT end)
 
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. More...
 
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. More...
 
- Public Member Functions inherited from stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >
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.
 
virtual std::unique_ptr< ExamInfoget_exam_info_uptr_for_target () const
 
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 &)
 
ParsingObjectoperator= (const ParsingObject &)
 
void ask_parameters ()
 
bool parse (std::istream &f)
 
bool parse (const char *const filename)
 
- 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.
 

Static Public Attributes

static const char *const registered_name = "PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion"
 Name which will be used when parsing a GeneralisedObjectiveFunction object.
 

Protected Member Functions

const TimeGateDefinitionsget_time_gate_definitions () const
 
bool actual_subsets_are_approximately_balanced (std::string &warning_message) const override
 Implementation of function that checks subset balancing. More...
 
void set_defaults () override
 Sets defaults before parsing.
 
void initialise_keymap () override
 sets parsing keys More...
 
bool post_processing () override
 This will be called at the end of the parsing. More...
 
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.
 
- Protected Member Functions inherited from stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >
void compute_sensitivities ()
 compute subset and total sensitivity 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.
 
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 More...
 
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 More...
 
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 More...
 
int output_image_size_xy
 the output image size in x and y direction More...
 
int output_image_size_z
 the output image size in z direction More...
 
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< ProjectorByBinPairprojector_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
 
- Protected Attributes inherited from stir::SumOfGeneralisedObjectiveFunctions< PoissonLogLikelihoodWithLinearModelForMeanAndProjData< TargetT >, TargetT, PoissonLogLikelihoodWithLinearModelForMean< TargetT > >
_functions_type _functions
 
- 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< 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. 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...
 
- 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. More...
 
- Protected Types inherited from stir::SumOfGeneralisedObjectiveFunctions< PoissonLogLikelihoodWithLinearModelForMeanAndProjData< TargetT >, TargetT, PoissonLogLikelihoodWithLinearModelForMean< TargetT > >
typedef std::vector< PoissonLogLikelihoodWithLinearModelForMeanAndProjData< TargetT > > _functions_type
 
typedef _functions_type::iterator _functions_iterator_type
 
typedef _functions_type::const_iterator _functions_const_iterator_type
 
- 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_lessRegistryType
 The type of the registry.
 
- Static Protected Member Functions inherited from stir::RegisteredObject< GeneralisedObjectiveFunction< TargetT > >
static RegistryTyperegistry ()
 Static function returning the registry. More...
 

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

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 stir::warning().

◆ 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.

Reimplemented from stir::SumOfGeneralisedObjectiveFunctions< PoissonLogLikelihoodWithLinearModelForMeanAndProjData< TargetT >, TargetT, PoissonLogLikelihoodWithLinearModelForMean< TargetT > >.

◆ 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.

Reimplemented from stir::SumOfGeneralisedObjectiveFunctions< PoissonLogLikelihoodWithLinearModelForMeanAndProjData< TargetT >, TargetT, PoissonLogLikelihoodWithLinearModelForMean< 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 >.

◆ 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

◆ 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 >.

◆ 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 stir::read_from_file(), and stir::warning().

◆ 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 >.

◆ 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 stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >::compute_sub_gradient_without_penalty_plus_sensitivity().

Referenced by stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >::compute_sub_gradient_without_penalty_plus_sensitivity().

Member Data Documentation

◆ _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()

◆ _additive_gated_proj_data_sptr

template<typename TargetT >
shared_ptr<GatedProjData> stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >::_additive_gated_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_filename_prefix

template<typename TargetT >
std::string stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >::_normalisation_filename_prefix
protected

the normalisation or/and attenuation data

◆ _motion_correction_type

template<typename TargetT >
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()

◆ 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

◆ output_image_size_z

template<typename TargetT >
int stir::PoissonLogLikelihoodWithLinearModelForMeanAndGatedProjDataWithMotion< TargetT >::output_image_size_z
protected

the output image size in z direction

convention: if -1, use default as provided by VoxelsOnCartesianGrid constructor


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