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Tesi etd-08292017-003402


Thesis type
Tesi di laurea magistrale
Author
BETTARINI, TOMMASO
URN
etd-08292017-003402
Title
Total variation regulated algorithm for PET image reconstruction
Struttura
FISICA
Corso di studi
FISICA
Commissione
relatore Prof. Belcari, Nicola
Parole chiave
  • quantitative imaging
  • regolarizzazione
  • regularization
  • tomografia ad emissione di positroni
  • positron emission tomography
Data inizio appello
20/09/2017;
Consultabilità
completa
Riassunto analitico
Positron emission tomography (PET) is a nuclear medical imaging technique<br>which allows non-invasive quantitative evaluation of biochemical and functional<br>processes by measuring the spatial distribution of radioactive tracers inside the<br>patient body. PET is used both in clinical and pre-clinical studies, for example<br>on small animals to test new drugs and radiotracers. Quantitative imaging is<br>one of the key features of PET imaging and deals with the determination of the<br>precise amount of radiotracer in each region of the body, other than its local-<br>ization.<br>The major limits in quantitative PET are the partial volume effect (PVE),<br>which causes an activity spill-over from the original radioactive object to the<br>surrounding voxels, and the presence of noise in the reconstructed image. This<br>problem can be dealt with through a proper regularization of the reconstruction<br>process. Image reconstruction consists in elaborating the acquired data in or-<br>der to obtain the original spatial distribution of the radioactive source and the<br>most common iterative algorithm for PET image reconstruction is the ML-EM<br>(Maximum Likelihood Expectation Maximization).<br>This thesis focuses on the development and the validation of a regulated<br>reconstruction algorithm for PET images. Total variation (TV) was chosen as<br>regularizing function, due to its behavior of smoothing uniform regions while<br>preserving strong edges. The code was written using the Insight Segmentation<br>and Registration Toolkit (ITK) and integrated in the existing ML-EM algo-<br>rithm developed by the Functional Imaging and Instrumentation Group (FIIG)<br>of the Department of Physics of the University of Pisa. In order to study the<br>proposed regularization, a custom phantom has been designed in conformity<br>with the specifications of the most popular protocols. The phantom was filled<br>with a radioactive source and simulated in the GATE environment along with<br>the geometry and the acquisition system of the Inviscan IRIS PET/CT scanner.<br>Results showed that the denoising of the reconstructed data with the pro-<br>posed algorithm can improve the precision on image quantification, albeit the<br>choice of the parameter governing the regularization may depends on image<br>features.
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