Line data    Source code

       1             : #pragma once
       2             : 
       3             : #include "Functional.h"
       4             : 
       5             : #include <memory>
       6             : 
       7             : namespace elsa
       8             : {
       9             :     /**
      10             :      * @brief Class representing a negative log-likelihood functional for transmission tomography.
      11             :      *
      12             :      * @author Matthias Wieczorek - initial code
      13             :      * @author Maximilian Hornung - modularization
      14             :      * @author Tobias Lasser - rewrite
      15             :      *
      16             :      * @tparam data_t data type for the domain of the residual of the functional, defaulting to
      17             :      * real_t
      18             :      *
      19             :      * The TransmissionLogLikelihood functional evaluates as \f$ \sum_{i=1}^n (b_i \exp(-x_i) + r_i)
      20             :      * - y_i\log(b_i \exp(-x_i) + r_i) \f$, with \f$ b=(b_i) \f$ denoting the mean number of photons
      21             :      * per detector (blank scan), \f$ y=(y_i) \f$ denoting the measurements, \f$ r=(r_i) \f$
      22             :      * denoting the mean number of background events, and \f$ x=(x_i) \f$.
      23             :      *
      24             :      * Typically, \f$ x \f$ is wrapped in a LinearResidual without a data vector, i.e. \f$ x \mapsto
      25             :      * Ax \f$.
      26             :      */
      27             :     template <typename data_t = real_t>
      28             :     class TransmissionLogLikelihood : public Functional<data_t>
      29             :     {
      30             :     public:
      31             :         /**
      32             :          * @brief Constructor for transmission log-likelihood, using y, b, and r (no residual)
      33             :          *
      34             :          * @param[in] domainDescriptor describing the domain of the functional
      35             :          * @param[in] y the measurement data vector
      36             :          * @param[in] b the blank scan data vector
      37             :          * @param[in] r the background event data vector
      38             :          */
      39             :         TransmissionLogLikelihood(const DataDescriptor& domainDescriptor,
      40             :                                   const DataContainer<data_t>& y, const DataContainer<data_t>& b,
      41             :                                   const DataContainer<data_t>& r);
      42             : 
      43             :         /**
      44             :          * @brief Constructor for transmission log-likelihood, using only y and b (no residual)
      45             :          *
      46             :          * @param[in] domainDescriptor describing the domain of the functional
      47             :          * @param[in] y the measurement data vector
      48             :          * @param[in] b the blank scan data vector
      49             :          */
      50             :         TransmissionLogLikelihood(const DataDescriptor& domainDescriptor,
      51             :                                   const DataContainer<data_t>& y, const DataContainer<data_t>& b);
      52             : 
      53             :         /**
      54             :          * @brief Constructor for transmission log-likelihood, using y, b, and r, and a residual as
      55             :          * input
      56             :          *
      57             :          * @param[in] residual to be used when evaluating the functional (or its derivative)
      58             :          * @param[in] y the measurement data vector
      59             :          * @param[in] b the blank scan data vector
      60             :          * @param[in] r the background event data vector
      61             :          */
      62             :         TransmissionLogLikelihood(const Residual<data_t>& residual, const DataContainer<data_t>& y,
      63             :                                   const DataContainer<data_t>& b, const DataContainer<data_t>& r);
      64             : 
      65             :         /**
      66             :          * @brief Constructor for transmission log-likelihood, using y and b, and a residual as
      67             :          * input
      68             :          *
      69             :          * @param[in] residual to be used when evaluating the functional (or its derivative)
      70             :          * @param[in] y the measurement data vector
      71             :          * @param[in] b the blank scan data vector
      72             :          */
      73             :         TransmissionLogLikelihood(const Residual<data_t>& residual, const DataContainer<data_t>& y,
      74             :                                   const DataContainer<data_t>& b);
      75             : 
      76             :         /// make copy constructor deletion explicit
      77             :         TransmissionLogLikelihood(const TransmissionLogLikelihood<data_t>&) = delete;
      78             : 
      79             :         /// default destructor
      80           0 :         ~TransmissionLogLikelihood() override = default;
      81             : 
      82             :     protected:
      83             :         /// the evaluation of the transmission log-likelihood
      84             :         data_t evaluateImpl(const DataContainer<data_t>& Rx) override;
      85             : 
      86             :         /// the computation of the gradient (in place)
      87             :         void getGradientInPlaceImpl(DataContainer<data_t>& Rx) override;
      88             : 
      89             :         /// the computation of the Hessian
      90             :         LinearOperator<data_t> getHessianImpl(const DataContainer<data_t>& Rx) override;
      91             : 
      92             :         /// implement the polymorphic clone operation
      93             :         TransmissionLogLikelihood<data_t>* cloneImpl() const override;
      94             : 
      95             :         /// implement the polymorphic comparison operation
      96             :         bool isEqual(const Functional<data_t>& other) const override;
      97             : 
      98             :     private:
      99             :         /// the measurement data vector y
     100             :         std::unique_ptr<const DataContainer<data_t>> _y{};
     101             : 
     102             :         /// the blank scan data vector b
     103             :         std::unique_ptr<const DataContainer<data_t>> _b{};
     104             : 
     105             :         /// the background event data vector r
     106             :         std::unique_ptr<const DataContainer<data_t>> _r{};
     107             :     };
     108             : } // namespace elsa