22 citations · 22 across the 4 of their papers we have counts for
4 papers
Reliable Evaluation of MRI Motion Correction: Dataset and Insights
Kun Wang, Tobit Klug, Stefan Ruschke +2
Correcting motion artifacts in MRI is important, as they can hinder accurate diagnosis. However, evaluating deep learning-based and classical motion correction methods remains fund…
MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRI
Tobit Klug, Kun Wang, Stefan Ruschke +1
A major challenge of the long measurement times in magnetic resonance imaging (MRI), an important medical imaging technology, is that patients may move during data acquisition. Thi…
Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods
Tobit Klug, Dogukan Atik, Reinhard Heckel
Supervised training of deep neural networks on pairs of clean image and noisy measurement achieves state-of-the-art performance for many image reconstruction tasks, but such traini…
Scaling Laws For Deep Learning Based Image Reconstruction
Tobit Klug, Reinhard Heckel
Deep neural networks trained end-to-end to map a measurement of a (noisy) image to a clean image perform excellent for a variety of linear inverse problems. Current methods are onl…