Preklinik uygulamalara yönelik çok parametreli nicel MRG haritalama ve işleme hatti

Quantitative MRI (qMRI) enables direct measurement of tissue properties -such as relaxation times and magnetic susceptibility- that shape the MRI signal in predictable ways. Unlike qualitative contrasts, qMRI provides reproducible biomarkers for studying processes from healthy aging to neurodegenerative and psychiatric disorders, with multiparametric approaches offering complementary insights as each parameter reflects distinct biological features. This thesis presents a multiparametric qMRI pipeline for preclinical use, demonstrated on 21 female Wistar rats at 7T. The pipeline integrates relaxometry mapping (T$_1$, T$_2$, R$_2^*$) within a Python-based framework featuring a GUI for subject-level analysis, and ensures reproducibility through automated conversion of raw data into BIDS-compliant format. Relaxometry maps were benchmarked against existing toolboxes with Bland-Altman analysis, confirming strong agreement. The pipeline was further extended to quantitative susceptibility mapping (QSM), where a dedicated phantom enabled evaluation of reconstruction algorithms; STAR-QSM outperformed alternatives by visual and nRMSE assessment and was adopted for preprocessing phase data and generating susceptibility maps with the Sepia toolbox. Regional mean susceptibility and relaxometry measures were extracted, with ROI-level mean, standard deviation, SEM, and voxel counts reported for transparency. Overall, this work establishes a reproducible preclinical qMRI framework unifying relaxometry and QSM, demonstrating methodological rigor and translational potential while providing a foundation for future studies of microstructural and physiological changes in disease and aging models.