Bilkent University
Department of Computer Engineering
CS 590/690 SEMINAR
Investigating PET Image Enhancement through Neural and Classical Optimal Transport Methods
Emir Türkölmez
Master Student
(Supervisor: Prof.Dr.Selim Aksoy)
Computer Engineering Department
Bilkent University
Abstract: Positron Emission Tomography (PET) provides critical functional insights for diagnosing conditions like cancer and neurological disorders but is hindered by low spatial resolution, limiting its ability to capture fine structural details. This study explores two methods to enhance PET image quality using their paired and registered high-resolution Magnetic Resonance (MR) image counterparts. Classical transport techniques, such as Earth Mover's Distance (EMD), solve linear optimization problems to align the PET intensity distribution with MR anatomical structures, offering precise and interpretable results. Neural optimal transport, on the other hand, is based on nonlinearity to learn complex, feature-rich mappings that capture semantic relationships. Through experiments on paired PET-MR datasets, we demonstrate that classical methods provide immediate, reliable transformations, while neural approaches offer the potential to capture intricate feature-level details. These complementary techniques highlight a promising path for improving PET image quality and enhancing its diagnostic utility in clinical practice. Keywords: Positron emission tomography, magnetic resonance imaging, image enhancement, neural networks, optimal transport, Earth Mover's Distance
DATE: December 09, Monday @ 15:30 Place: EA 502