Description of the research project:
Magnetic resonance imaging (MRI) is a versatile and widely used imaging technique that enables visualization of different anatomical structures and physiological processes inside the body. A key disadvantage of MRI, however, is that data acquisition is typically slow, resulting in long per-patient scan times and low overall patient throughput. The challenge is even greater when imaging dynamic objects such as the beating heart, where motion during data acquisition introduces inconsistencies in the data and, consequently, errors in the reconstructed images.
To address this, a well-established strategy is to acquire only a reduced amount of data during an MR measurement and to compensate for the missing information using either hand-crafted or learned imaging models that incorporate prior knowledge about the structure of typical images.
Our research group is actively working in this field, particularly in the context of dynamic MRI. In our work, we have for example developed novel image reconstruction methods that reduce MRI data acquisition times by a factor of eight or more, without compromising the quality or diagnostic value of the reconstructed images.
Associated publications:
[KHKOBS2017]
Florian Knoll and Martin Holler and Thomas Koesters and Richardo Otazo and Kristian Bredies and Daniel K Sodickson. Joint MR-PET reconstruction using a multi-channel image regularizer.
IEEE Transactions on Medical Imaging, 36(1):1-16,
2017
[SHSBS2017]
Matthias Schloegl and Martin Holler and Andreas Schwarzl and Kristian Bredies and Rudolf Stollberger. Infimal convolution of total generalized variation functionals for dynamic MRI.
Magnetic Resonance in Medicine, 78(1):142-155,
2017
[LSHBS2019]
Andreas Lesch and Matthias Schloegl and Martin Holler and Kristian Bredies and Rudolf Stollberger. Ultrafast 3D Bloch-Siegert B1+-mapping using variational modeling.
Magnetic Resonance in Medicine, 81(2):881-892,
2019
[AHKL2023]
Abdullah, Abdullah and Holler, Martin and Kunisch, Karl and Landman, Malena Sabate. Latent-Space Disentanglement with Untrained Generator Networks for the Isolation of Different Motion Types in Video Data.
Scale Space and Variational Methods in Computer Vision,
Springer International Publishing,
2023
[KHKBS2016]
Florian Knoll and Martin Holler and Thomas Koesters and Kristian Bredies and Daniel K.~Sodickson. Simultaneous PET-MRI reconstruction with vectorial second order total generalized variation.
Proceedings of the 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC),
2016
