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Project

System design and image reconstruction for unconventional geometry PET scanner.

Nowadays, tomographic imaging is one of the mast fundamental tools to early and accurate diagnosis and evaluation of the therapy effectiveness of an important numbers of diseases, such as cardiac or oncological. In this thesis project, the development of effective reconstruction algorithms will be explored, in order to obtain high quality and accurate images in a reasonable period of time (seconds). Although this thesis project objective is the PET image reconstruction, the mathematical methods can be easily applied to other techniques like Single-Photon Emission Computed Tomography (SPECT) or Computed Tomography (CT scan). The objective of Positron Emission Tomography (PET) is to generate images of a radioactive tracer inside certain parts of the human body. This tracer is a beta+ (positron), and its interaction with the electrons from the human body generate two photons of 5llkeV that propagate in opposite directions and that can be detected in coincidence in a PET detector. The advantages of this technique are the fact that it is not invasive and offers inner body metabolic information a useful tool to diagnosis and follow-up of the disease of interest. Data acquired in PET systems need to be processed in a reconstruction process, involving analytical algorithms as well as statistical iterative ones. The most common algorithm used in PET image reconstruction is the Maximum Likelihood Expectation Maximization (MLEM). This algorithm needs to wait until the data acquisition has ended to begin the reconstruction process, and also needs a complete matrix modeling of the system, that makes the processing slow. The majority of emission photons are absorbed or scattered from their original trajectory due to media attenuation. This leads to a considerably quantity of events lost or deviating from their theoretical collinearity, implying the addition of undesired noise and artifacts to the image. These undesired effects have to be corrected for by using a reconstruction algorithms that accounts for these effects via prior information as CT scans or via using Time of Flight (TOF) information as MLPA algorithm does. The implementation of scatter and attenuation correction and the normalization in the sensitivity are essential in PET detectors in order to improve image quality. PET detectors crystals and systems used to present systematic and random errors that also have to be corrected. Depending on the scanner geometry certain lines-of-response (LOR) efficiencies will be affected, producing subsequent count-rate variability that leads on a inaccurate image. This undesired effect can be corrected using normalization algorithms that exploits geometry symmetry prior knowledge to estimate the correction factors in a fast and efficient way. The most important parameters for characterizing a PET image are the spatial resolution, the sensitivity and the quantification of real activity. In a few words, spatial resolution allows the localization of small lesions and identifications of individual lesions even if they are close to each other, a high sensitivity means that good image quality can be obtained even with low tracer doses, and activity quantification is the capacity of recovering the real activity in regions of interest in the image. Monte Carlo simulations are an important tool to evaluate and optimize the correction methods in image PET. These simulations are generally used to study the photon trajectories and their interactions with the tissues in the human body. By defining the simulated detector characteristics based on calibration measurements, very realistic simulations are obtained. New designed PETs systems are being designed, such as open-geometry systems or systems dedicated to imaging a particular organ. For several reasons, these systems are often unable to perform transmission measurements. In spite of these difficulties, such dedicated PET systems have many advantages, including better spatial resolution, a reduced production cost and optimal performance for particular imaging tasks, enabling dose and/or scan time reduction. In our project, we aim to obtain a cardiac PET image, building a dedicated PET system that consist on four detector plates with TOF information. This system will have to deal with the limited angle in tomography, so an accurate implementation of normalization for the detector sensitivities and correction for attenuation and scatter will be decisive. New norrnalization algorithms will have to be designed to correct the artifacts in the image due to its certain geometry, making new assumptions that differs from the ring-normalization that commonly appears in the literature. Also, the fact of not having CT scan or MR to give further information for this corrections will make the TOF information decisive to correct attenuation and minimizing the loss of resolution. Also, the scatter correction will be studied and take into account for the heart tissue structure. At the end of this project, we aim to obtain a prototype that can offer real measurements from a cardiac phantom, and to explore new ways to correct image in limited angle TOF-PET systems

Date:1 May 2019 →  28 Feb 2022
Keywords:pet, medicalimaging
Disciplines:Nuclear imaging
Project type:PhD project