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Prof. Dr. rer. nat. Matthias Günther. Deputy Institute Director. Fraunhofer Institute for Digital Medicine MEVIS Max-von-Laue-Str. 2 28359 Bremen. Phone +49 421 218 ...
- Prof. Günther Receives Funding From VolkswagenStiftung
Professor Matthias Günther from the Department of Physics /...
- About Mevis
The Fraunhofer Institute for Digital Medicine MEVIS develops...
- Reference Projects
Modern Deep Learning methods allow AI models to be trained...
- Imprint
Imprint - Prof. Dr. rer. nat. Matthias Günther - Fraunhofer...
- Prof. Günther Receives Funding From VolkswagenStiftung
Matthias Günther. Recommended implementation of arterial spin‐labeled perfusion MRI for clinical applications: a consensus of the ISMRM perfusion study group and the European consortium for ASL …. DC Alsop, JA Detre, X Golay, M Günther, J Hendrikse, ...
Frontiers in Neuroimaging – Neuroimaging Analysis and Protocols 2. Huber J, Eickel K, Günther M (2023) U-JET: Preliminary results of a convolutional neural network approach for distortion-free image reconstruction of PROPELLER data. Proceedings of the ISMRM & ISMRT Annual Meeting & Exhibition. 3709.
Matthias Günther Objectives One challenge in arterial spin labeling (ASL) is the high variability of arterial transit times (ATT), which causes associated arterial transit delay (ATD) artifacts.
Dec 4, 2020 · "The loudest noise during measurement is usually produced when the images are recorded," says Prof. Matthias Günther, deputy director at Fraunhofer MEVIS. "We are working on using metamaterials ...
Jun 5, 2020 · The May 2020 MRM Highlights Reproducible Research Insights interview is with Cristoffer Cordes and Matthias Günther, researchers at Fraunhofer Institute for Digital Medicine MEVIS in Bremen, Germany, and authors of a paper entitled “Portable and platform‐independent MR pulse sequence programs”. This paper was chosen as the MRM Highlights ...
The Fraunhofer Institute for Digital Medicine MEVIS develops innovative software solutions for more precise diagnoses and more effective therapies. The possibilities are as varied as the challenges: Artificial intelligence and self-learning algorithms will bring enormous advancements to the clinical routine in hospitals and medical practices.
