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Tesi etd-08292017-003402

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