Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Kerstin Hammernik

Showing results (1-10 of 27) with videos related to

Pageof 3
Sort By:
IEEE Transactions on Medical Imaging|February 14, 2024
Motion-Compensated MR CINE Reconstruction With Reconstruction-Driven Motion EstimationJiazhen Pan, Wenqi Huang, Daniel Rueckert, et al.
Magnetic Resonance in Medicine|January 29, 2024
Predictive uncertainty in deep learning-based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data setThomas Küstner, Kerstin Hammernik, Daniel Rueckert, et al.
Medical Image Analysis|November 4, 2023
Unrolled and rapid motion-compensated reconstruction for cardiac CINE MRIJiazhen Pan, Manal Hamdi, Wenqi Huang, et al.
IEEE Transactions on Medical Imaging|September 10, 2021
Bayesian Uncertainty Estimation of Learned Variational MRI ReconstructionDominik Narnhofer, Alexander Effland, Erich Kobler, et al.
Magnetic Resonance in Medicine|June 10, 2021
Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combinationKerstin Hammernik, Jo Schlemper, Chen Qin, et al.
Magnetic Resonance in Medicine|November 9, 2017
Learning a variational network for reconstruction of accelerated MRI dataKerstin Hammernik, Teresa Klatzer, Erich Kobler, et al.
Scientific Reports|November 6, 2020
Rapid mono and biexponential 3D-T<sub>1ρ</sub> mapping of knee cartilage using variational networksMarcelo V W Zibetti, Patricia M Johnson, Azadeh Sharafi, et al.
IEEE Transactions on Medical Imaging|October 13, 2023
Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive ReviewVeronika Spieker, Hannah Eichhorn, Kerstin Hammernik, et al.
IEEE Signal Processing Magazine|June 12, 2023
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imagingKerstin Hammernik, Thomas Küstner, Burhaneddin Yaman, et al.
Magnetic Resonance in Medicine|May 19, 2018
Assessment of the generalization of learned image reconstruction and the potential for transfer learningFlorian Knoll, Kerstin Hammernik, Erich Kobler, et al.
Pageof 3

Showing results (1-10 of 27) with videos related to

Sort By:
Pageof 3
IEEE Transactions on Medical Imaging|February 14, 2024
Motion-Compensated MR CINE Reconstruction With Reconstruction-Driven Motion EstimationJiazhen Pan, Wenqi Huang, Daniel Rueckert, et al.
Magnetic Resonance in Medicine|January 29, 2024
Predictive uncertainty in deep learning-based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data setThomas Küstner, Kerstin Hammernik, Daniel Rueckert, et al.
Medical Image Analysis|November 4, 2023
Unrolled and rapid motion-compensated reconstruction for cardiac CINE MRIJiazhen Pan, Manal Hamdi, Wenqi Huang, et al.
IEEE Transactions on Medical Imaging|September 10, 2021
Bayesian Uncertainty Estimation of Learned Variational MRI ReconstructionDominik Narnhofer, Alexander Effland, Erich Kobler, et al.
Magnetic Resonance in Medicine|June 10, 2021
Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combinationKerstin Hammernik, Jo Schlemper, Chen Qin, et al.
Magnetic Resonance in Medicine|November 9, 2017
Learning a variational network for reconstruction of accelerated MRI dataKerstin Hammernik, Teresa Klatzer, Erich Kobler, et al.
Scientific Reports|November 6, 2020
Rapid mono and biexponential 3D-T<sub>1ρ</sub> mapping of knee cartilage using variational networksMarcelo V W Zibetti, Patricia M Johnson, Azadeh Sharafi, et al.
IEEE Transactions on Medical Imaging|October 13, 2023
Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive ReviewVeronika Spieker, Hannah Eichhorn, Kerstin Hammernik, et al.
IEEE Signal Processing Magazine|June 12, 2023
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imagingKerstin Hammernik, Thomas Küstner, Burhaneddin Yaman, et al.
Magnetic Resonance in Medicine|May 19, 2018
Assessment of the generalization of learned image reconstruction and the potential for transfer learningFlorian Knoll, Kerstin Hammernik, Erich Kobler, et al.
Pageof 3