Michael Hintermüller, Christoph Kolbitsch, Tobias Schäffter
01.01.2022 − 31.12.2023
Emerging methods for clinical imaging aim to identify quantitative tissue parameter maps rather than qualitative gray scale images to boost the amount of information available for medical diagnoses.
Figure 1: Tissue parameter map obtained through quantitative magnetic resonance imaging.
In this project, a variational framework for inferring quantitative parameter maps directly from undersampled magnetic resonance imaging data in the k-space is proposed and investigated. Special emphasis lies on data-driven corrections to the possibly oversimplified physical models that constrain the inverse problem and on machine learning optimal regularization terms. The developed methods are integrated in a dynamic imaging framework with motion correction to deliver spatially and temporally varying parameter maps relevant for clinical applications like cardiac magnetic resonance imaging.