Project
AA5-7 (was EF3-12)
Integrated Learning and Variational Methods for Quantitative Dynamic Imaging
Project Heads
Michael Hintermüller, Christoph Kolbitsch, Tobias Schäffter
Project Members
Moritz Flaschel
Project Duration
01.01.2022 − 31.12.2023
Located at
WIAS
Description
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.
External Website
Selected Publications