• pet
• system model
• image quality
• montecarlo

Positron Emission Tomography (PET) is a nuclear medicine technique which allows
to determine the in-vivo spatial and temporal distribution of a radiotracer inside an
examined subject. PET has applications both in clinical and pre-clinical studies,
where in the latter it is used as a research tool for drug development and targeting
and development on small animal subjects like mice and rats.
The process of image formation starting from the data acquired by a PET scan-
ner is called image reconstruction and, nowadays, can be performed with the use
of iterative algorithms. These algorithms offer superior results at the expense of a
high computational cost, in terms of calculation time and memory requirements,
and can be performed only on modern workstations. With the use of the itera-
tive methods the quality of the reconstructed image relies to a great extent on the
accuracy of the so-called system model (or system matrix), which defines the re-
lationship between the object space and the measurement space. The creation
of the system model is a computational challenge and it is often subdivided into
factors that can be computed separately to reduce the time needed to perform its
construction.
This thesis focuses on the creation and the validation of an accurate factorized
system model for the INVISCAN PET/CT IRIS scanner. The model is made of
two components, an analytical component called geometric matrix and a second
component, called detector matrix, which models the interactions of the photons
inside the detector modules in order to take into account the penetration and inter-
scatter effects that can occur in the crystals matrix of the detector modules. The
geometric, the detector and the system matrices cannot be created as dense matrices
due to their huge number of elements. The whole process of data manipulation has
been performed with the use of sparse matrix dedicated algorithms.
The first part of the thesis focuses on the creation of the system model of the
scanner. First the detector matrix is created starting from a GEANT4 Monte Carlo
(MC) simulation and then the system matrix is assembled joining the geometric and
the detector matrices. Giving the high number of lines of responses (LOR) of the
scanner, the MC needs to be optimized to obtain a reasonable total simulation time.
The optimization is performed by exploiting the symmetries of the scanner and by
tuning the parameters of the simulation for each LOR. The detector matrix can be
created with different levels of accuracy that strongly affect the memory footprint
of the whole system model, which, in turn, affect the computational requirements
of the entire image reconstruction platform. Three models of different accuracy are
here proposed.
The second part of the thesis focuses on the comparison between the currently
used system model of the IRIS scanner and the new models. The comparison
has been performed analyzing the Image Quality phantom described in the NEMA
NU 4 - 2008 protocol Performance Evaluation of small animals Positron Emission
Tomographs. The images reconstruction has been performed on real data of the
phantom acquired within the IRIS scanner in possession of the Istituto di Fisiologia
Clinica - CNR Pisa.
The results showed that the new factorized models improves the noise of the
reconstructed images and the so-called recovery coefficients, that are related to the
spatial resolution of the scanner.