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master's thesis
Rekonstrukcija MRI slika mozga pomoću fastMRI biblioteke
2024. urn:nbn:hr:200:243073
Bertić, Dražen
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Visual Computing

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Bertić, D. (2024). Rekonstrukcija MRI slika mozga pomoću fastMRI biblioteke (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:243073

Bertić, Dražen. "Rekonstrukcija MRI slika mozga pomoću fastMRI biblioteke." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2024. https://urn.nsk.hr/urn:nbn:hr:200:243073

Bertić, Dražen. "Rekonstrukcija MRI slika mozga pomoću fastMRI biblioteke." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2024. https://urn.nsk.hr/urn:nbn:hr:200:243073

Bertić, D. (2024). 'Rekonstrukcija MRI slika mozga pomoću fastMRI biblioteke', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 08 February 2025, https://urn.nsk.hr/urn:nbn:hr:200:243073

Bertić D. Rekonstrukcija MRI slika mozga pomoću fastMRI biblioteke [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2024 [cited 2025 February 08] Available at: https://urn.nsk.hr/urn:nbn:hr:200:243073

D. Bertić, "Rekonstrukcija MRI slika mozga pomoću fastMRI biblioteke", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:200:243073

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Metadata
TitleRekonstrukcija MRI slika mozga pomoću fastMRI biblioteke
Title (english)Reconstruction of brain MRI images using the fastMRI
AuthorDražen Bertić
MentorIrena Galić (mentor)
MentorMarija Habijan (sumentor)
Committee memberIrena Galić (predsjednik povjerenstva)
Committee memberKrešimir Nenadić (član povjerenstva)
Committee memberMarija Habijan (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Visual Computing)
Osijek
Defense date and country2024-12-16, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
sažetak (hrvatski): Ovaj se rad bavi rekonstrukcijom slika magnetske rezonance mozga primjenom metoda dubokog učenja implementiranih u fastMRI biblioteku. Magnetska rezonanca je ključna metoda za di- jagnostiku u medicini. Cilj ovog rada je istražti primjene tehnike prijenosa učenja na unaprijed istreniranim U-Net i End-to-End VarNet modelima kako bi se poboljšala kvaliteta rekonstrukcije slika uz poduzorkovane podatke. Korišteni su fastMRI i Calgary-Campinas skupovi podataka, a podatci su pripremljeni uz primjenu različitih maski poduzorkovanja ekvidistantnih, slučajnih ekvidistantnih i maski s Gaussovim šumom. Modeli su trenirani i evaluirani pomoću SSIM i PSNR metrika. Rezultati pokazuju da prijenosom učenja možemo poboljšati kvalitetu rekons- trukcije, pogotovo na End-to-End VarNet modelu. Kvaliteta rekonstrukcija još uvijek ne doseže razinu potrebnu za kliničku upotrebu.
Abstract (english)
This paper addresses the reconstruction of magnetic resonance images using deep learning met- hods implemented in fastMRI library. Magnetic resonance is a key diagnostic tool in medicine. The aim of this paper is to research uses of transfer learning techniques on pre-trained U-Net and End-to- End VarNet models to improve the quality of image reconstruction from undersam- pled data. fastMRI and Calgary-Campinas datasets were used, and data was prepared using multiple undersampling masks: equispaced, random equispaced and masks with Gaussian no- ise. The models were trained and evaluated using SSIM and PSNR metrics. Results show that transfer learning can enhance reconstruction quality, particularly for the End-to-End VarNet model. However, the quality of reconstructions still falls short of the level required for clinical use.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:243073
Study programmeTitle: Graduate University Study Programme in Computer Engineering
Study programme type: university
Study level: graduate
Academic / professional title: sveučilišni/a magistar/magistra inženjer/inženjerka računarstva (sveučilišni/a magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-12-20 12:15:03