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2020 – today
- 2024
[j9]Jiazhen Pan
, Manal Hamdi, Wenqi Huang, Kerstin Hammernik, Thomas Küstner, Daniel Rueckert:
Unrolled and rapid motion-compensated reconstruction for cardiac CINE MRI. Medical Image Anal. 91: 103017 (2024)- [j8]Inês Machado, Esther Puyol-Antón, Kerstin Hammernik, Gastão Cruz, Devran Ugurlu, Ihsane Olakorede, Ilkay Öksüz, Bram Ruijsink, Miguel Castelo-Branco, Alistair A. Young, Claudia Prieto, Julia A. Schnabel, Andrew P. King:
A Deep Learning-Based Integrated Framework for Quality-Aware Undersampled Cine Cardiac MRI Reconstruction and Analysis. IEEE Trans. Biomed. Eng. 71(3): 855-865 (2024) - [j7]Veronika Spieker, Hannah Eichhorn, Kerstin Hammernik, Daniel Rueckert, Christine Preibisch, Dimitrios C. Karampinos, Julia A. Schnabel:
Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review. IEEE Trans. Medical Imaging 43(2): 846-859 (2024) - [j6]Jiazhen Pan, Wenqi Huang, Daniel Rueckert, Thomas Küstner, Kerstin Hammernik:
Motion-Compensated MR CINE Reconstruction With Reconstruction-Driven Motion Estimation. IEEE Trans. Medical Imaging 43(7): 2420-2433 (2024) - [j5]Aya Ghoul, Jiazhen Pan, Andreas Lingg, Jens Kübler, Patrick Krumm, Kerstin Hammernik, Daniel Rueckert, Sergios Gatidis, Thomas Küstner:
Attention-Aware Non-Rigid Image Registration for Accelerated MR Imaging. IEEE Trans. Medical Imaging 43(8): 3013-3026 (2024) - [c32]Patrick T. Haft, Wenqi Huang, Gastão Cruz, Daniel Rueckert, Veronika A. Zimmer, Kerstin Hammernik:
Neural Implicit k-space with Trainable Periodic Activation Functions for Cardiac MR Imaging. Bildverarbeitung für die Medizin 2024: 82-87 - [c31]Yundi Zhang, Nil Stolt Ansó, Jiazhen Pan, Wenqi Huang, Kerstin Hammernik, Daniel Rueckert:
Direct Cardiac Segmentation from Undersampled K-Space using Transformers. ISBI 2024: 1-4 - [c30]Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks, Kilian Weiss, Christine Preibisch, Julia A. Schnabel:
Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI. MICCAI (7) 2024: 562-571 - [c29]Veronika Spieker, Hannah Eichhorn, Jonathan K. Stelter, Wenqi Huang, Rickmer F. Braren, Daniel Rueckert, Francisco Sahli Costabal, Kerstin Hammernik, Claudia Prieto, Dimitrios C. Karampinos, Julia A. Schnabel:
Self-supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations. MICCAI (7) 2024: 614-624 - [i28]Veronika Spieker, Hannah Eichhorn, Jonathan K. Stelter, Wenqi Huang, Rickmer F. Braren, Daniel Rückert, Francisco Sahli Costabal, Kerstin Hammernik, Claudia Prieto, Dimitrios C. Karampinos, Julia A. Schnabel:
Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation. CoRR abs/2404.08350 (2024) - [i27]Aya Ghoul, Jiazhen Pan, Andreas Lingg, Jens Kübler, Patrick Krumm, Kerstin Hammernik, Daniel Rueckert, Sergios Gatidis, Thomas Küstner:
Attention-aware non-rigid image registration for accelerated MR imaging. CoRR abs/2404.17621 (2024) - [i26]Yundi Zhang, Nil Stolt Ansó, Jiazhen Pan, Wenqi Huang, Kerstin Hammernik, Daniel Rueckert:
Direct Cardiac Segmentation from Undersampled K-space Using Transformers. CoRR abs/2406.00192 (2024) - [i25]Siying Xu, Kerstin Hammernik, Andreas Lingg, Jens Kübler, Patrick Krumm, Daniel Rueckert, Sergios Gatidis, Thomas Küstner:
Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine Imaging. CoRR abs/2407.03034 (2024) - [i24]Aya Ghoul, Kerstin Hammernik, Andreas Lingg, Patrick Krumm, Daniel Rueckert, Sergios Gatidis, Thomas Küstner:
Highly efficient non-rigid registration in k-space with application to cardiac Magnetic Resonance Imaging. CoRR abs/2410.18834 (2024) - 2023
- [j4]Kerstin Hammernik, Thomas Küstner, Burhaneddin Yaman, Zhengnan Huang, Daniel Rueckert, Florian Knoll, Mehmet Akçakaya:
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) - [c28]Wenqi Huang, Hongwei Bran Li, Jiazhen Pan, Gastão Cruz, Daniel Rueckert, Kerstin Hammernik:
Neural Implicit k-Space for Binning-Free Non-Cartesian Cardiac MR Imaging. IPMI 2023: 548-560 - [c27]Leonhard F. Feiner, Martin J. Menten, Kerstin Hammernik, Paul Hager, Wenqi Huang, Daniel Rueckert, Rickmer F. Braren, Georgios Kaissis:
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines. UNSURE@MICCAI 2023: 1-11 - [c26]Hannah Eichhorn, Kerstin Hammernik, Veronika Spieker, Samira M. Epp, Daniel Rueckert, Christine Preibisch, Julia A. Schnabel:
Physics-Aware Motion Simulation For T2*-Weighted Brain MRI. SASHIMI@MICCAI 2023: 42-52 - [c25]Veronika A. Zimmer, Kerstin Hammernik, Vasiliki Sideri-Lampretsa, Wenqi Huang, Anna Reithmeir, Daniel Rueckert, Julia A. Schnabel:
Towards Generalised Neural Implicit Representations for Image Registration. DGM4MICCAI 2023: 45-55 - [c24]Denis Prokopenko, Kerstin Hammernik, Thomas A. Roberts, David F. A. Lloyd, Daniel Rueckert, Joseph V. Hajnal:
The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning. PIPPI@MICCAI 2023: 64-74 - [c23]Veronika Spieker, Wenqi Huang, Hannah Eichhorn, Jonathan K. Stelter, Kilian Weiss, Veronika A. Zimmer, Rickmer F. Braren, Dimitrios C. Karampinos, Kerstin Hammernik, Julia A. Schnabel:
ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space. DGM4MICCAI 2023: 183-192 - [c22]Jiazhen Pan, Suprosanna Shit, Özgün Turgut, Wenqi Huang, Hongwei Bran Li, Nil Stolt Ansó, Thomas Küstner, Kerstin Hammernik, Daniel Rueckert:
Global k-Space Interpolation for Dynamic MRI Reconstruction Using Masked Image Modeling. MICCAI (10) 2023: 228-238 - [c21]Nil Stolt Ansó, Julian McGinnis, Jiazhen Pan, Kerstin Hammernik, Daniel Rueckert:
NISF: Neural Implicit Segmentation Functions. MICCAI (4) 2023: 734-744 - [i23]Jiazhen Pan, Wenqi Huang, Daniel Rueckert, Thomas Küstner, Kerstin Hammernik:
Reconstruction-driven motion estimation for motion-compensated MR CINE imaging. CoRR abs/2302.02504 (2023) - [i22]Veronika Spieker, Hannah Eichhorn, Kerstin Hammernik, Daniel Rueckert, Christine Preibisch, Dimitrios C. Karampinos, Julia A. Schnabel:
Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review. CoRR abs/2305.06739 (2023) - [i21]Jiazhen Pan, Suprosanna Shit, Özgün Turgut, Wenqi Huang, Hongwei Bran Li, Nil Stolt Ansó, Thomas Küstner, Kerstin Hammernik, Daniel Rueckert:
Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling. CoRR abs/2307.12672 (2023) - [i20]Denis Prokopenko, Kerstin Hammernik, Thomas A. Roberts, David F. A. Lloyd, Daniel Rueckert, Joseph V. Hajnal:
The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning. CoRR abs/2308.07885 (2023) - [i19]Veronika Spieker, Wenqi Huang, Hannah Eichhorn, Jonathan K. Stelter, Kilian Weiss, Veronika A. Zimmer, Rickmer F. Braren, Dimitrios C. Karampinos, Kerstin Hammernik, Julia A. Schnabel:
ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space. CoRR abs/2308.08830 (2023) - [i18]Leonhard F. Feiner, Martin J. Menten, Kerstin Hammernik, Paul Hager, Wenqi Huang, Daniel Rueckert, Rickmer F. Braren, Georgios Kaissis:
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines. CoRR abs/2309.16831 (2023) - 2022
- [j3]Dominik Narnhofer, Alexander Effland, Erich Kobler, Kerstin Hammernik, Florian Knoll, Thomas Pock:
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction. IEEE Trans. Medical Imaging 41(2): 279-291 (2022) - [c20]Huaqi Qiu, Kerstin Hammernik, Chen Qin, Chen Chen, Daniel Rueckert:
Embedding Gradient-Based Optimization in Image Registration Networks. MICCAI (6) 2022: 56-65 - [c19]Moritz Binzer, Kerstin Hammernik, Daniel Rueckert, Veronika A. Zimmer:
Long-Term Cognitive Outcome Prediction in Stroke Patients Using Multi-task Learning on Imaging and Tabular Data. PRIME@MICCAI 2022: 137-148 - [c18]Jiazhen Pan, Daniel Rueckert, Thomas Küstner, Kerstin Hammernik:
Learning-Based and Unrolled Motion-Compensated Reconstruction for Cardiac MR CINE Imaging. MICCAI (6) 2022: 686-696 - [i17]Helena Klause, Alexander Ziller, Daniel Rueckert, Kerstin Hammernik, Georgios Kaissis:
Differentially private training of residual networks with scale normalisation. CoRR abs/2203.00324 (2022) - [i16]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) - [i15]Inês Prata Machado, Esther Puyol-Antón, Kerstin Hammernik, Gastão Cruz, Devran Ugurlu, Ihsane Olakorede, Ilkay Öksüz, Bram Ruijsink, Miguel Castelo-Branco, Alistair A. Young, Claudia Prieto, Julia A. Schnabel, Andrew P. King:
A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis. CoRR abs/2205.01673 (2022) - [i14]Jiazhen Pan, Daniel Rueckert, Thomas Küstner, Kerstin Hammernik:
Learning-based and unrolled motion-compensated reconstruction for cardiac MR CINE imaging. CoRR abs/2209.03671 (2022) - [i13]Wenqi Huang, Hongwei Li, Gastão Cruz, Jiazhen Pan, Daniel Rueckert, Kerstin Hammernik:
Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imaging. CoRR abs/2212.08479 (2022) - 2021
- [c17]Kerstin Hammernik, Jiazhen Pan, Daniel Rueckert, Thomas Küstner:
Motion-Guided Physics-Based Learning for Cardiac MRI Reconstruction. ACSCC 2021: 900-907 - [c16]Patrick Henriksen, Kerstin Hammernik, Daniel Rueckert, Alessio Lomuscio:
Bias Field Robustness Verification of Large Neural Image Classifiers. BMVC 2021: 202 - [c15]Jiazhen Pan, Daniel Rueckert, Thomas Küstner, Kerstin Hammernik:
Efficient Image Registration Network for Non-Rigid Cardiac Motion Estimation. MLMIR@MICCAI 2021: 14-24 - [c14]Patricia M. Johnson, Geunu Jeong, Kerstin Hammernik, Jo Schlemper, Chen Qin, Jinming Duan, Daniel Rueckert, Jingu Lee, Nicola Pezzotti, Elwin de Weerdt, Sahar Yousefi, Mohamed S. Elmahdy, Jeroen Hendrikus Franciscus Van Gemert, Christophe Schülke, Mariya Doneva, Tim Nielsen, Sergey Kastryulin, Boudewijn P. F. Lelieveldt, Matthias J. P. van Osch, Marius Staring, Eric Z. Chen, Puyang Wang, Xiao Chen, Terrence Chen, Vishal M. Patel, Shanhui Sun, Hyungseob Shin, Yohan Jun, Taejoon Eo, Sewon Kim, Taeseong Kim, Dosik Hwang, Patrick Putzky, Dimitrios Karkalousos, Jonas Teuwen, Nikita Miriakov, Bart Bakker, Matthan W. A. Caan, Max Welling, Matthew J. Muckley, Florian Knoll:
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 - [c13]Chen Chen, Kerstin Hammernik, Cheng Ouyang, Chen Qin, Wenjia Bai, Daniel Rueckert:
Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation. MICCAI (3) 2021: 149-159 - [c12]Huaqi Qiu, Chen Qin, Andreas Schuh, Kerstin Hammernik, Daniel Rueckert:
Learning Diffeomorphic and Modality-invariant Registration using B-splines. MIDL 2021: 645-664 - [c11]Inês Machado, Esther Puyol-Antón, Kerstin Hammernik, Gastão Cruz, Devran Ugurlu, Bram Ruijsink, Miguel Castelo-Branco, Alistair A. Young, Claudia Prieto, Julia A. Schnabel, Andrew P. King:
Quality-Aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled K-Space Data. STACOM@MICCAI 2021: 12-20 - [c10]Devran Ugurlu, Esther Puyol-Antón, Bram Ruijsink, Alistair A. Young, Inês Machado, Kerstin Hammernik, Andrew P. King, Julia A. Schnabel:
The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images. STACOM@MICCAI 2021: 57-65 - [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]Chen Chen, Kerstin Hammernik, Cheng Ouyang, Chen Qin, Wenjia Bai, Daniel Rueckert:
Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation. CoRR abs/2107.01079 (2021) - [i10]Inês Machado, Esther Puyol-Antón, Kerstin Hammernik, Gastão Cruz, Devran Ugurlu, Bram Ruijsink, Miguel Castelo-Branco, Alistair A. Young, Claudia Prieto, Julia A. Schnabel, Andrew P. King:
Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data. CoRR abs/2109.07955 (2021) - [i9]Devran Ugurlu, Esther Puyol-Antón, Bram Ruijsink, Alistair A. Young, Inês Machado, Kerstin Hammernik, Andrew P. King, Julia A. Schnabel:
The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images. CoRR abs/2109.13230 (2021) - [i8]Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kerstin Hammernik, Daniel Rueckert, Georgios Kaissis:
Complex-valued deep learning with differential privacy. CoRR abs/2110.03478 (2021) - [i7]Huaqi Qiu, Kerstin Hammernik, Chen Qin, Daniel Rueckert:
GraDIRN: Learning Iterative Gradient Descent-based Energy Minimization for Deformable Image Registration. CoRR abs/2112.03915 (2021) - 2020
- [j2]Florian Knoll, Kerstin Hammernik, Chi Zhang, Steen Moeller, Thomas Pock, Daniel K. Sodickson, Mehmet Akçakaya:
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) - [c9]Thomas Küstner, Jiazhen Pan, Christopher Gilliam, Haikun Qi, Gastão Cruz, Kerstin Hammernik, Bin Yang, Thierry Blu, Daniel Rueckert, René M. Botnar, Claudia Prieto, Sergios Gatidis:
Deep-learning based motion-corrected image reconstruction in 4D magnetic resonance imaging of the body trunk. APSIPA 2020: 976-985 - [i6]Chen Qin, Jo Schlemper, Kerstin Hammernik, Jinming Duan, Ronald M. Summers, Daniel Rueckert:
Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction. CoRR abs/2007.05993 (2020) - [i5]Oliver Maier, Steven H. Baete, Alexander Fyrdahl, Kerstin Hammernik, Seb Harrevelt, Lars Kasper, Agah Karakuzu, Michael Loecher, Franz Patzig, Ye Tian, Ke Wang, Daniel Gallichan, Martin Uecker, Florian Knoll:
CG-SENSE revisited: Results from the first ISMRM reproducibility challenge. CoRR abs/2008.04308 (2020)
2010 – 2019
- 2019
- [c8]Patricia M. Johnson, Matthew J. Muckley, Mary Bruno, Erich Kobler, Kerstin Hammernik, Thomas Pock, Florian Knoll:
Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions. MLMIR@MICCAI 2019: 71-79 - [p1]Kerstin Hammernik:
Variationsnetzwerke für die medizinische Bildrekonstruktion. Ausgezeichnete Informatikdissertationen 2019: 109-118 - [i4]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) - [i3]Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, Kerstin Hammernik:
Σ-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction. CoRR abs/1912.05480 (2019) - [i2]Kerstin Hammernik, Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, Daniel Rueckert:
Σ-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction. CoRR abs/1912.09278 (2019) - 2018
- [c7]Erich Kobler, Matthew J. Muckley, Baiyu Chen, Florian Knoll, Kerstin Hammernik, Thomas Pock, Daniel K. Sodickson, Ricardo Otazo:
Variational Deep Learning for Low-Dose Computed Tomography. ICASSP 2018: 6687-6691 - [c6]Franz Thaler, Kerstin Hammernik, Christian Payer, Martin Urschler, Darko Stern:
Sparse-View CT Reconstruction Using Wasserstein GANs. MLMIR@MICCAI 2018: 75-82 - 2017
- [c5]Kerstin Hammernik, Tobias Würfl, Thomas Pock, Andreas K. Maier:
A Deep Learning Architecture for Limited-Angle Computed Tomography Reconstruction. Bildverarbeitung für die Medizin 2017: 92-97 - [c4]Teresa Klatzer, Daniel Soukup, Erich Kobler, Kerstin Hammernik, Thomas Pock:
Trainable Regularization for Multi-frame Superresolution. GCPR 2017: 90-100 - [c3]Erich Kobler, Teresa Klatzer, Kerstin Hammernik, Thomas Pock:
Variational Networks: Connecting Variational Methods and Deep Learning. GCPR 2017: 281-293 - [i1]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) - 2016
- [j1]Jianhua Yao, Joseph E. Burns, Daniel Forsberg, Alexander Seitel, Abtin Rasoulian, Purang Abolmaesumi, Kerstin Hammernik, Martin Urschler, Bulat Ibragimov, Robert Korez, Tomaz Vrtovec, Isaac Castro-Mateos, Jose M. Pozo, Alejandro F. Frangi, Ronald M. Summers, Shuo Li:
A multi-center milestone study of clinical vertebral CT segmentation. Comput. Medical Imaging Graph. 49: 16-28 (2016) - [c2]Teresa Klatzer, Kerstin Hammernik, Patrick Knöbelreiter, Thomas Pock:
Learning joint demosaicing and denoising based on sequential energy minimization. ICCP 2016: 1-11 - 2015
- [c1]Martin Urschler, Kerstin Hammernik, Thomas Ebner, Darko Stern:
Automatic Intervertebral Disc Localization and Segmentation in 3D MR Images Based on Regression Forests and Active Contours. CSI@MICCAI 2015: 130-140
Coauthor Index
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