Generation of distortion-corrected BOLD fMRI images without the presence of magnetic field mapsNikolov, Ivan (Avtor) Demšar, Jure (Mentor) functional MRIBOLDsusceptibility distortion correctiondeep learningimage-to-image translationHuman Connectome ProjectSusceptibility-induced distortions degrade EPI image quality and can misalign functional images with anatomical scans. Correcting these distortions is therefore important for reliable downstream analysis. Classical correction methods require additional acquisitions, such as field maps or reversed phase-encoding images, which are often unavailable in legacy datasets or time-constrained clinical settings. This thesis investigates whether deep learning can correct susceptibility-induced distortions from a single distorted image and a T1-weighted anatomical scan. We compare several neural network architectures using data from three Human Connectome Project cohorts. We then evaluate the selected MedNeXt v1 Medium model in a complete 4D fMRI pipeline. The proposed approach achieves the best RMSE, SSIM, and NMI across all cohorts. It outperforms the uncorrected baseline, SynBOLD-DisCo, and a topup-refined variant of itself. It performs well across all evaluated phase-encoding directions. Inference requires only a single feed-forward pass and can run on a standard CPU without a dedicated GPU.20262026-10-15 10:15:34Magistrsko delo/naloga189779sl