Siemens Healthineers (Germany)

Industry / private company


Location: Forchheim u.a., Germany (DE) DE

ISNI: 0000000405524145

ROR: https://ror.org/0449c4c15

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Types of publications

Journal article
Book chapter / Article in edited volumes
Authored book
Translation
Thesis
Edited Volume
Conference contribution
Other publication type
Unpublished / Preprint

Publication year

From
To

Abstract

Journal

Estimating the Execution Time of CNN Inference on GPUs (2024) Groth S, Schmid M, Teich J, Hannig F Conference contribution, Original article Combining Pose Estimation and inertial measurement tracking data for a biomechanical simulation of river wave surfing using optimal control (2024) Weiß A, Koelewijn A, Masmoudi I, Lluch È 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 Assessment of Deep Learning-based Reconstruction with Imperfect Ground Truth for MRCP (2024) Kim J, Nickel MD, Knoll F Conference contribution, Conference Contribution 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 Computing Null Space Hallucinations on Simulated SPECT Image Reconstructions (2023) Nau M, Reymann M, Massanes F, Vija AH, Maier A Conference contribution, Abstract of a poster Simulation-driven training of vision transformers enables metal artifact reduction of highly truncated CBCT scans (2023) Fan F, Ritschl L, Beister M, Biniazan R, Wagner F, Kreher BW, Gottschalk T, et al. Journal article, Original article