Paolo Garelli
Self-Supervised, Deep Learning Denoising of GRAPPATINI T2-Maps in Magnetic Resonance Imaging.
Rel. Filippo Molinari, Tom Hilbert, Thomas Yu. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2025
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Abstract
Magnetic Resonance Imaging (MRI) is a widely used medical imaging modality, offering non-invasive and non-ionizing diagnostic capabilities. However, conventional MRI relies on contrast-weighted images influenced by various physical parameters and scanner characteristics, making their interpretation still highly qualitative and subjective. This approach limits analysis only to relative changes in intensities or morphological features and no absolute comparisons can be performed. Quantitative MRI (qMRI) is shifting this perspective by enabling the generation of parameter-specific maps, such as relaxation times T1 and T2 maps, capable of providing absolute and quantitative information, enabling longitudinal studies, inter-subject comparisons, and the establishment of normative ranges. Specifically, T2 relaxation times have been shown to be highly sensitive to physiological and pathological changes, such as intra- and extra-cellular water accumulation and myelin loss.
Despite these advantages, gold standard T2 mapping techniques are still constrained by long acquisition times, impacting patient comfort and increasing the susceptibility to motion artifacts
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