Prof. Dr. Florian Knoll

Professur für Computational Imaging

Comparative Study on Co-registration Techniques for Diffusion-Weighted Breast MRI and Improved ADC Mapping (2024) Brock L, Liebert A, Schreiter H, Skwierawska D, Ehring C, Eberle J, Laun FB, et al. Conference contribution Training deep learning reconstruction models for radial real-time cardiac cine MRI using synthetic golden-angle data (2024) Ott Y, Vornehm M, Wetzl J, Giese D, Knoll F Conference contribution, Abstract of lecture How to best match voxels - evaluating different sequential co-registration strategies for ultra-high b-value DWI in multiparametric breast MRI (2024) Brock L, Liebert A, Schreiter H, Ehring C, Eberle J, Laun FB, Uder M, et al. Conference contribution, Abstract of a poster Assessment of Deep Learning-based Reconstruction with Imperfect Ground Truth for MRCP (2024) Kim J, Nickel MD, Knoll F Conference contribution, Conference Contribution Digital reference object toolkit of breast DCE MRI for quantitative evaluation of image reconstruction and analysis methods (2024) Bae J, Tan Z, Solomon E, Huang Z, Heacock L, Moy L, Knoll F, Kim SG Journal article Deep learning-based image reconstruction for higher resolution cardiac T1 mapping (2024) Amsel D, Vornehm M, Wetzl J, Schmidt M, Tillmanns C, Gebker R, Giese D, et al. Conference contribution, Abstract of a poster Variational Network Meets Conjugate Gradient: Inline Reconstruction and Strain Analysis of Accelerated Cardiac Cine MRI (2024) Vornehm M, Wetzl J, Fürnrohr F, Giese D, Ahmad R, Knoll F Conference contribution, Abstract of a poster Low-Latency Reconstruction of Real-Time Cine MRI Using an Unrolled Network (2024) Vornehm M, Wetzl J, Fürnrohr F, Giese D, Ahmad R, Knoll F Conference contribution, Abstract of lecture Deep Learning-based Reconstruction of Accelerated MR Cholangiopancreatography (2024) Kim J, Nickel MD, Knoll F Conference contribution, Conference Contribution Analysis of Deep Learning-based Reconstruction Models for Highly Accelerated MR Cholangiopancreatography: to Fine-tune or not to Fine-tune (2023) Kim J, Benkert T, Riemenschneider B, Nickel MD, Knoll F Conference contribution, Abstract of a poster