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master's thesis
Rekonstrukcija PET slika suparničkim modelima učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:224000
Kuljanac, Marija
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Kuljanac, M. (2022). Rekonstrukcija PET slika suparničkim modelima učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:224000

Kuljanac, Marija. "Rekonstrukcija PET slika suparničkim modelima učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:224000

Kuljanac, Marija. "Rekonstrukcija PET slika suparničkim modelima učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:224000

Kuljanac, M. (2022). 'Rekonstrukcija PET slika suparničkim modelima učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 30 August 2024, https://urn.nsk.hr/urn:nbn:hr:168:224000

Kuljanac M. Rekonstrukcija PET slika suparničkim modelima učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 August 30] Available at: https://urn.nsk.hr/urn:nbn:hr:168:224000

M. Kuljanac, "Rekonstrukcija PET slika suparničkim modelima učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:224000

Metadata
TitleRekonstrukcija PET slika suparničkim modelima učenja
Title (english)Reconstruction of PET Images Using Adversarial Learning Models
AuthorMarija Kuljanac
MentorDamir Seršić (mentor)
Committee memberDamir Seršić (predsjednik povjerenstva)
Committee memberMarko Subašić (član povjerenstva)
Committee memberKrešimir Križanović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-12, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
PET predstavlja jednu od tehnika snimanja medicinskih slika te je u ovom diplomskom radu dan pregled teorijskih osnova nekih od najpoznatijih metoda njihove rekonstrukcije, FBP i MLEM. Zbog nedostataka upravo navedenih algoritama rezultati se pokušavaju poboljšati kombinacijom konvencionalnih metoda s metodama dubokog učenja, u ovom slučaju s generativnim suparničkim modelima. Dane su osnove strojnog učenja uz detaljniji pregled dvaju arhitektura generativnih neuronskih mreža - mreže za super rezoluciju (SRGAN) i rekonstrukciju (GANrec) kojima je zadatak poboljšati kvalitetu slika dobivenih na ulazu. Opisane su implementacije tih modela uz njihovu evaluaciju na parovima podataka koji se sastoje od slika iz online baze podataka i slika na kojima su simulirani nedostaci uređaja i klasičnih metoda rekonstrucije.
Abstract (english)
PET represents one of the medical imaging techniques and this master's thesis gives a theoretical overview of some of the most known methods of PET image reconstruction, FBP and MLEM. Because the methods mentioned have their faults there's an attempt to improve their results by combining them with deep learning, in this case with generative adversarial models. The basics of machine learning are given along with a detailed overview of two generative neural network architectures – super resolution generative network (SRGAN) and generative network for reconstruction (GANrec) whose main task is to improve the quality of input images. The implementations of these models are described as well as their evaluation on pairs of data made of images from the online database and simulated images that capture the imperfections of devices and ordinary reconstruction methods.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:224000
Study programmeTitle: Information and Communication Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije (magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2023-01-14 09:14:27