3 papers
cs.CV2026
CUPA-T2*: Covariance-Aware Uncertainty Propagation and Alignment for T2* Mapping in Accelerated MRI
Gideon N. L. Rouwendaal, Natascha Niessen, Hannah Eichhorn +3
Quantitative T2* maps have strong potential for biomarker discovery but are limited by long scan times, rendering them impractical in clinical settings. Significant acceleration ca…
eess.IV2025
Motion-Robust T2* Quantification from Gradient Echo MRI with Physics-Informed Deep Learning
Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik +5
Purpose: T2* quantification from gradient echo magnetic resonance imaging is particularly affected by subject motion due to the high sensitivity to magnetic field inhomogeneities,…
eess.IV2024
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…