Bayesian Data Analysis for quantitative Magnetic Resonance Fingerprinting

Abstract - Magnetic Resonance Imaging (MRI) is a medical imaging technique which is widely used in clinical practice. Usually, only qualitative images are obtained. The goal in quantitative MRI (qMRI) is a quantitative determination of tissue- related parameters. In 2013, Magnetic Resonance Fingerprinting (MRF) was introduced as a fast method for qMRI which simultaneously estimates the parameters of interest. In this talk, I will present main results of my PhD thesis in which I applied Bayesian methods for the data analysis of MRF. A novel, Bayesian uncertainty analysis for the conventional MRF method is introduced as well as a new MRF approach in which the data are modelled directly in the Fourier domain. Furthermore, results from both MRF approaches will be compared with regard to various aspects.

Biographie - Selma Metzner studied Mathematics at Friedrich-Schiller-University in Jena. She then started her PhD at PTB Berlin and successfully defended her thesis in September 2021. Currently she is working on a DFG project with the title: Bayesian compressed sensing for nanoscale chemical mapping in the mid- infrared regime.