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Florian Knoll
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- affiliation: New York University School of Medicine, New York City, NY, USA
- affiliation (PhD): Graz University of Technology, Austria
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2020 – today
- 2024
- [c12]Moritz Zaiss, Junaid R. Rajput, Hoai Nam Dang, Vladimir Golkov, Daniel Cremers, Florian Knoll, Andreas K. Maier:
Exploring GPT-4 as MR Sequence and Reconstruction Programming Assistant - GPT4MR. Bildverarbeitung für die Medizin 2024: 94-99 - [c11]Florian Fürnrohr
, Jens Wetzl
Data Consistent Variational Networks for Zero-shot Self-supervised MR Reconstruction. Bildverarbeitung für die Medizin 2024: 316-321 - [c10]Junaid R. Rajput, Simon Weinmueller, Jonathan Endres, Peter Dawood, Florian Knoll, Andreas K. Maier, Moritz Zaiss:
Death by Retrospective Undersampling - Caveats and Solutions for Learning-Based MRI Reconstructions. MICCAI (7) 2024: 233-241 - [c9]Erik Gösche
Domain Influence in MRI Medical Image Segmentation: Spatial Versus k-Space Inputs. MLMI@MICCAI (1) 2024: 310-319 - [c8]Luise Brock, Andrzej Liebert, Hannes Schreiter, Dominika Skwierawska, Chris Ehring, Jessica Eberle, Frederik B. Laun, Michael Uder, Lorenz A. Kapsner, Sabine Ohlmeyer, Dominique Hadler, Florian Knoll, Sebastian Bickelhaupt:
Comparative Study on Co-registration Techniques for Diffusion-Weighted Breast MRI and Improved ADC Mapping. WBIR 2024: 127-136 - [i16]Jinho Kim, Marcel Dominik Nickel
Accelerated MR Cholangiopancreatography with Deep Learning-based Reconstruction. CoRR abs/2405.03732 (2024) - [i15]Eddy Solomon, Patricia M. Johnson, Zhengguo Tan, Radhika Tibrewala, Yvonne W. Lui, Florian Knoll, Linda Moy, Sungheon Gene Kim, Laura Heacock:
fastMRI Breast: A publicly available radial k-space dataset of breast dynamic contrast-enhanced MRI. CoRR abs/2406.05270 (2024) - 2023
- [j13]Kerstin Hammernik
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging. IEEE Signal Process. Mag. 40(1): 98-114 (2023) - [j12]Martin Zach
Stable Deep MRI Reconstruction Using Generative Priors. IEEE Trans. Medical Imaging 42(12): 3817-3832 (2023) - 2022
- [j11]Marcelo Victor Wüst Zibetti
Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications. IEEE Trans. Computational Imaging 8: 449-461 (2022) - [j10]Dominik Narnhofer
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction. IEEE Trans. Medical Imaging 41(2): 279-291 (2022) - [i14]Kerstin Hammernik, Thomas Küstner, Burhaneddin Yaman, Zhengnan Huang, Daniel Rueckert, Florian Knoll, Mehmet Akçakaya:
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging. CoRR abs/2203.12215 (2022) - [i13]Martin Zach
Stable deep MRI reconstruction using Generative Priors. CoRR abs/2210.13834 (2022) - 2021
- [j9]Matthew J. Muckley
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE Trans. Medical Imaging 40(9): 2306-2317 (2021) - [c7]Patricia M. Johnson, Geunu Jeong, Kerstin Hammernik, Jo Schlemper, Chen Qin
Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge. MLMIR@MICCAI 2021: 25-34 - [c6]Zhengnan Huang, Jonghyun Bae
A Simulation Pipeline to Generate Realistic Breast Images for Learning DCE-MRI Reconstruction. MLMIR@MICCAI 2021: 45-53 - [i12]Dominik Narnhofer, Alexander Effland, Erich Kobler, Kerstin Hammernik, Florian Knoll, Thomas Pock:
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction. CoRR abs/2102.06665 (2021) - [i11]Ruiyang Zhao, Burhaneddin Yaman, Yuxin Zhang, Russell Stewart, Austin Dixon, Florian Knoll, Zhengnan Huang, Yvonne W. Lui, Michael S. Hansen, Matthew P. Lungren:
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data. CoRR abs/2109.03812 (2021) - [i10]Marcelo V. W. Zibetti, Florian Knoll, Ravinder R. Regatte:
Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications. CoRR abs/2110.14703 (2021) - 2020
- [j8]Florian Knoll
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues. IEEE Signal Process. Mag. 37(1): 128-140 (2020) - [c5]Anuroop Sriram
End-to-End Variational Networks for Accelerated MRI Reconstruction. MICCAI (2) 2020: 64-73 - [i9]Florian Knoll, Tullie Murrell, Anuroop Sriram, Nafissa Yakubova, Jure Zbontar, Michael G. Rabbat, Aaron Defazio, Matthew J. Muckley, Daniel K. Sodickson, C. Lawrence Zitnick, Michael P. Recht:
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. CoRR abs/2001.02518 (2020) - [i8]Anuroop Sriram, Jure Zbontar, Tullie Murrell, Aaron Defazio, C. Lawrence Zitnick, Nafissa Yakubova, Florian Knoll, Patricia M. Johnson:
End-to-End Variational Networks for Accelerated MRI Reconstruction. CoRR abs/2004.06688 (2020) - [i7]Oliver Maier, Steven H. Baete, Alexander Fyrdahl
CG-SENSE revisited: Results from the first ISMRM reproducibility challenge. CoRR abs/2008.04308 (2020) - [i6]Matthew J. Muckley, Bruno Riemenschneider
State-of-the-Art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge. CoRR abs/2012.06318 (2020)
2010 – 2019
- 2019
- [c4]Patricia M. Johnson, Matthew J. Muckley
Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions. MLMIR@MICCAI 2019: 71-79 - [e2]Florian Knoll, Andreas Maier, Daniel Rueckert, Jong Chul Ye:
Machine Learning for Medical Image Reconstruction - Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings. Lecture Notes in Computer Science 11905, Springer 2019, ISBN 978-3-030-33842-8 [contents] - [i5]Florian Knoll, Kerstin Hammernik, Chi Zhang, Steen Moeller, Thomas Pock, Daniel K. Sodickson, Mehmet Akçakaya:
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction. CoRR abs/1904.01112 (2019) - 2018
- [j7]Georg Schramm
Evaluation of Parallel Level Sets and Bowsher's Method as Segmentation-Free Anatomical Priors for Time-of-Flight PET Reconstruction. IEEE Trans. Medical Imaging 37(2): 590-603 (2018) - [c3]Erich Kobler, Matthew J. Muckley
Variational Deep Learning for Low-Dose Computed Tomography. ICASSP 2018: 6687-6691 - [e1]Florian Knoll
Machine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings. Lecture Notes in Computer Science 11074, Springer 2018, ISBN 978-3-030-00128-5 [contents] - [i4]Sunli Tang, Carlos Fernandez-Granda, Sylvain Lannuzel, Brett Bernstein, Riccardo Lattanzi, Martijn A. Cloos, Florian Knoll, Jakob Assländer:
Multicompartment Magnetic Resonance Fingerprinting. CoRR abs/1802.10492 (2018) - [i3]Jure Zbontar, Florian Knoll, Anuroop Sriram, Matthew J. Muckley, Mary Bruno, Aaron Defazio, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzal, Adriana Romero, Michael G. Rabbat, Pascal Vincent, James Pinkerton, Duo Wang, Nafissa Yakubova, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, Yvonne W. Lui:
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI. CoRR abs/1811.08839 (2018) - 2017
- [j6]Florian Knoll
Joint MR-PET Reconstruction Using a Multi-Channel Image Regularizer. IEEE Trans. Medical Imaging 36(1): 1-16 (2017) - [i2]Kerstin Hammernik, Teresa Klatzer, Erich Kobler, Michael P. Recht, Daniel K. Sodickson, Thomas Pock, Florian Knoll:
Learning a Variational Network for Reconstruction of Accelerated MRI Data. CoRR abs/1704.00447 (2017) - 2015
- [c2]Martin Benning
Preconditioned ADMM with Nonlinear Operator Constraint. System Modelling and Optimization 2015: 117-126 - [c1]Daniel K. Sodickson, Li Feng, Florian Knoll
The rapid imaging renaissance: sparser samples, denser dimensions, and glimmerings of a grand unified tomography. Biomedical Applications in Molecular, Structural, and Functional Imaging 2015: 94170G - 2014
- [j5]Tilman J. Sumpf, Andreas Petrovic, Martin Uecker, Florian Knoll
Fast T2 Mapping With Improved Accuracy Using Undersampled Spin-Echo MRI and Model-Based Reconstructions With a Generating Function. IEEE Trans. Medical Imaging 33(12): 2213-2222 (2014) - [i1]Tilman J. Sumpf, Andreas Petrovic, Martin Uecker, Florian Knoll, Jens Frahm:
Fast T2 Mapping with Improved Accuracy Using Undersampled Spin-echo MRI and Model-based Reconstructions with a Generating Function. CoRR abs/1405.3574 (2014) - 2013
- [j4]Manuel Freiberger, Florian Knoll
The Agile Library for Biomedical Image Reconstruction Using GPU Acceleration. Comput. Sci. Eng. 15(1): 34-44 (2013) - [j3]Tuomo Valkonen
Total Generalized Variation in Diffusion Tensor Imaging. SIAM J. Imaging Sci. 6(1): 487-525 (2013) - 2012
- [j2]Stephen L. Keeling, Christian Clason
An image space approach to Cartesian based parallel MR imaging with total variation regularization. Medical Image Anal. 16(1): 189-200 (2012) - 2011
- [j1]Stephen L. Keeling, Michael Hintermüller
A total variation based approach to correcting surface coil magnetic resonance images. Appl. Math. Comput. 218(2): 219-232 (2011)
Coauthor Index
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