Fast Field-Cycling (FFC) MRI generates images with T1-dispersion contrast that provide new insights for medical applications. No model of such dispersion data exists for biological tissues therefore a phenomenological approach is chosen here that minimises data loss while isolating meaningful information by curve fitting. This approach provided promising biomarkers in several pilot studies spanning a range of applications: osteoarthritis, liver fibrosis, breast cancer, glioma and fatty tissues. This shows that a dispersion-based approach of FFC-MRI data is an interesting and novel approach for the discovery of novel biomarkers.
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