Stefan Ruschke — accepted AI-conference papers

2026

Reliable Evaluation of MRI Motion Correction: Dataset and Insights

Kun Wang, Tobit Klug, Stefan Ruschke, Jan Kirschke, Reinhard Heckel

ICLR 2026poster

Correcting motion artifacts in scientific and medical imaging is important, as they significantly impact image quality. However, evaluating deep learning-based and classical motion correction methods remains fundamentally difficult due to the lack of accessible ground-truth target data. To address…

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2024

MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRI

Tobit Klug, Kun Wang, Stefan Ruschke, Reinhard Heckel

NeurIPS 2024poster

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. This leads to severe motion artifacts in the reconstructed images and volumes. In this paper, we propose MotionTTT a deep l…

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