Monthly stipend and accommodation support during M.Sc. by Scientific and Technological Research Council of Turkey (TUBITAK)
Full tuition waiver and stipend during M.Sc by Bilkent University

Research Excellence Award
Social Justice and Sustainability Award
High Honor Student at Bilkent University
Bilkent University Merit Scholarship
Summa Cum Laude

International Genetically Engineered Machine (iGEM) golden award (international synthetic biology competition organized by iGEM Foundation, Boston, USA).
METU High-school Merit Scholarship

This study examines a deep learning method for EPI susceptibility correction that merges physics-constrained learning with phase-injected formulation and two-step training, and assesses its impact on diffusion metrics. It offers quick and effective correction, suitable for clinical and research use.

Diffusion MRI (dMRI) has enabled the study of the brain’s complex architecture in vivo. However, resolving fiber structures at a finer detail necessitates denser q-space sampling, which can be impractical due to excessively long scan times . Deep learning methods for angular q-space interpolation show potential for reduced scan time by enabling q-space downsampling. However, these methods often demand large training data and pose challenges when dealing with non-conforming data, such as in pathological cases.

We propose a subject-specific, unsupervised Q-space Upsampling via physics-Constrained Coordinate-based Implicit network (QUCCI) that angularly interpolates q-space data. QUCCI combines implicit multilayer perceptron regularization with physics-driven spherical harmonic (SH) regularization and image regularization in the context of dMRI. In comparison to traditional and other deep learning methods, QUCCI demonstrates superior angular interpolation for highly undersampled q-space acquisitions.

In Magnetic Particle Imaging (MPI), the distribution of magnetic nanoparticles (MNPs) is imaged by moving a field free point (FFP) in space. All MNPs in close vicinity of the FFP contribute to the signal induced on the receive coil. The relaxation behavior of these MNPs are subject to a DC field due to the selection field (SF). In this work, we investigate the effects of the DC field on the relaxation behavior of the MNPs, with the goal of understanding the differences between the measured relaxations in Magnetic Particle Spectrometer (MPS) setups vs. MPI scanners.