Julian Hoßbach

Lehrstuhl für Informatik 5 (Mustererkennung) Institute of Radiology

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 Severe MR Motion Artefact Correction with 2 step Deep Learning-based guidance (2023) Hoßbach J, Splitthoff DN, Cauley S, Clifford B, Polak D, Maier A Conference contribution, Abstract of a poster Robust multi-contrast MRI denoising using trainable bilateral filters without noise-free targets (2023) Pfaff L, Wagner F, Hoßbach J, Preuhs E, Maul N, Thies M, Denzinger F, et al. Conference contribution Quantitative evaluation of denoising algorithms without noise-free ground-truth data (2023) Pfaff L, Wagner F, Hoßbach J, Preuhs E, Nickel MD, Wuerfl T, Maier A Conference contribution Unsupervised denoising of prostate DWI (2023) Pfaff L, Wagner F, Hoßbach J, Preuhs E, Gadjimuradov F, Benkert T, Nickel MD, et al. Conference contribution Deep learning-based motion quantification from k-space for fast model-based magnetic resonance imaging motion correction (2022) Hoßbach J, Splitthoff DN, Cauley S, Clifford B, Polak D, Lo WC, Meyer H, Maier A Journal article Training a tunable, spatially-adaptive denoiser without clean targets (2022) Pfaff L, Hoßbach J, Preuhs E, Arroyo Camejo S, Nickel MD, Maier A, Wuerfl T Conference contribution Prospective motion assessment within multi-shot imaging using coil mixing of the data consistency error and deep learning (2021) Hoßbach J, Splitthoff D, Clifford B, Polak D, Cauley S, Maier A Conference contribution MoPED: Motion Parameter Estimation DenseNet for accelerating retrospective motion correction (2020) Hoßbach J, Splitthoff D, Cauley S, Maier A Conference contribution, Conference Contribution Deep OCT angiography image generation for motion artifact suppression (2020) Hoßbach J, Husvogt L, Kraus M, Fujimoto JG, Maier A Conference contribution