Niñerola Baizán, AidaFarré Melero, ArnauRomero Díaz, Jacobo2026-02-182026-02-182026-01https://hdl.handle.net/2445/227011Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2026, Tutors: Aida Niñerola Baizán, Arnau Farré MeleroMagnetic Resonance Imaging (MRI) is a non-invasive diagnostic imaging modality that employs multiple acquisition sequences to generate complementary tissue contrasts. However, in clinical practice, not all sequences are available due to time, cost, or patient-related issues. In this work, we investigate deep learning–based supervised image-to-image translation for synthetic cross-sequence MRI generation using a dataset of 148 patients. T2, FLAIR, and contrast-enhanced T1 (T1GD) images are synthesized from T1 inputs using U-Net–based encoder–decoder models and conditional GANs (pix2pix), under full-resolution and patch-based training strategies. Evaluation using Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) shows that UNet models with tailored loss functions achieve results comparable to state-of-the-art approaches, outperforming pix2pix. Finally, qualitative evaluation revealed that conventional metrics fail to capture sequence-specific localized regions, highlighting the need for task-aware evaluation criteria.6 p.application/pdfengcc-by-nc-nd (c) Romero, 2026http://creativecommons.org/licenses/by-nc-nd/4.0/Ressonància magnèticaFísica mèdicaTreballs de fi de grauMagnetic resonanceMedical physicsBachelor's thesesinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/openAccess