Mathematical Methods in MRI - SFB MR-DYNAMO Workshop

Session

Contributed Talks: Session 6

21 May 2026, 14:30

HS BMT (BMTEG138), Stremayrgasse 16

Conveners

Contributed Talks: Session 6

• Martin Holler (University of Graz)

22. Zero-shot self-supervised learning of spatio-temporally varying regularisation parameter maps for dynamic cardiac MR image reconstruction

Dr Kostas Papafitsoros (Queen Mary University of London)

21/05/2026, 14:30

Data-driven reconstruction methods, mostly based on deep learning, have emerged as the undisputed state-of-the-art across many different inverse problems, including MRI reconstruction. However, they are also notorious for requiring large and diverse datasets to be trained successfully, preferably in a supervised manner. When target reconstructions cannot be obtained, zero-shot self-supervised...

27. Gabor primitives for accelerated cardiac cine MRI reconstruction

Mr Wenqi Huang (Technical University of Munich)

21/05/2026, 14:55

Accelerated cardiac cine MRI requires reconstructing spatiotemporal images from highly undersampled k-space data. Implicit neural representations (INRs) enable scan-specific reconstruction without large training datasets, but encode content implicitly in network weights without physically interpretable parameters. Gaussian primitives provide an explicit and geometrically interpretable...