Author

Julia A. Schnabel

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Preprint Aug 2026

CUPA-T2: Covariance-Aware Uncertainty Propagation and Alignment for T2 Mapping in Accelerated MRI

CUPA-T2* enables reconstruction uncertainty-aware T2* fitting and delivers voxel-wise uncertainty maps to support the interpretation of quantitative T2* estimates, while also revealing a trade-off with calibration (ECE) and selective prediction performance (AURC).

Gideon N. L. Rouwendaal, Natascha Niessen, H. Eichhorn et al. · 0 citations

#machine learning Preprint Aug 2026

Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction

This work proposes a multi-dimensional, primitive based framework for dynamic contrast-enhanced MRI reconstruction that disentangles the underlying anatomy, the dynamic contrast enhancement, and residual motion into separate temporal basis functions, thereby enabling a geometrical interpretation of the representation.

Veronika Spieker, Wenqi Huang, Cemre Ariyurek et al. · 0 citations

Julia A. Schnabel

CUPA-T2*: Covariance-Aware Uncertainty Propagation and Alignment for T2* Mapping in Accelerated MRI

Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction

CUPA-T2: Covariance-Aware Uncertainty Propagation and Alignment for T2 Mapping in Accelerated MRI