Laura Pfaff

Lehrstuhl für Informatik 5 (Mustererkennung) Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) (LME)

Virtual DSA for Learning Contrast Agent Dynamics in Projection Space (2025) Maul N, Birkhold A, Thies M, Vysotskaya N, Wagner F, Pfaff L, Kowarschik M, Maier A Conference contribution Enhancing diffusion-weighted prostate MRI through self-supervised denoising and evaluation (2024) Pfaff L, Darwish O, Wagner F, Thies M, Vysotskaya N, Hoßbach J, Weiland E, et al. Journal article Differentiable Score-Based Likelihoods: Learning CT Motion Compensation from Clean Images (2024) Thies M, Maul N, Mei S, Pfaff L, Vysotskaya N, Gu M, Utz J, et al. Conference contribution On the influence of smoothness constraints in computed tomography motion compensation (2024) Thies M, Wagner F, Maul N, Mei S, Gu M, Pfaff L, Vysotskaya N, et al. Conference contribution, Original article Self-supervised MRI denoising: leveraging Stein’s unbiased risk estimator and spatially resolved noise maps (2023) Pfaff L, Hoßbach J, Preuhs E, Wagner F, Arroyo Camejo S, Kannengiesser S, Nickel D, et al. Journal article Gradient-based geometry learning for fan-beam CT reconstruction (2023) Thies M, Wagner F, Maul N, Folle L, Meier M, Rohleder M, Schneider LS, et al. Journal article, Original article Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography (2023) Wagner F, Thies M, Maul N, Pfaff L, Aust O, Pechmann S, Syben C, Maier A Conference contribution, Conference Contribution On the Benefit of Dual-Domain Denoising in a Self-Supervised Low-Dose CT Setting (2023) Wagner F, Thies M, Pfaff L, Aust O, Pechmann S, Weidner D, Maul N, et al. Conference contribution Optimizing CT Scan Geometries With and Without Gradients (2023) Thies M, Wagner F, Maul N, Pfaff L, Schneider LS, Syben C, Maier A Conference contribution, Original article Noise2Contrast: Multi-contrast Fusion Enables Self-supervised Tomographic Image Denoising (2023) Wagner F, Thies M, Pfaff L, Maul N, Pechmann S, Gu M, Utz J, et al. Conference contribution