8 citations · 9 across the 8 of their papers we have counts for
4 papers · 1 filter
From Model Based to Learned Regularization in Medical Image Registration: A Comprehensive Review
Anna Reithmeir, Veronika Spieker, Vasiliki Sideri-Lampretsa +3
Image registration is fundamental in medical imaging applications, such as disease progression analysis or radiation therapy planning. The primary objective of image registration i…
Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation
Veronika Spieker, Hannah Eichhorn, Jonathan K. Stelter +8
Neural implicit k-space representations have shown promising results for dynamic MRI at high temporal resolutions. Yet, their exclusive training in k-space limits the application o…
Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI
Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik +4
We propose PHIMO, a physics-informed learning-based motion correction method tailored to quantitative MRI. PHIMO leverages information from the signal evolution to exclude motion-c…
Learning Physics-Inspired Regularization for Medical Image Registration with Hypernetworks
Anna Reithmeir, Julia A. Schnabel, Veronika A. Zimmer
Medical image registration aims at identifying the spatial deformation between images of the same anatomical region and is fundamental to image-based diagnostics and therapy. To da…