Search Results - "Hammernik, Kerstin"

Learning a variational network for reconstruction of accelerated MRI data by Hammernik, Kerstin, Klatzer, Teresa, Kobler, Erich, Recht, Michael P., Sodickson, Daniel K., Pock, Thomas, Knoll, Florian

“…Purpose To allow fast and high‐quality reconstruction of clinical accelerated multi‐coil MR data by learning a variational network that combines the…”
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Assessment of the generalization of learned image reconstruction and the potential for transfer learning by Knoll, Florian, Hammernik, Kerstin, Kobler, Erich, Pock, Thomas, Recht, Michael P, Sodickson, Daniel K

“…Purpose Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness…”
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CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions by Küstner, Thomas, Fuin, Niccolo, Hammernik, Kerstin, Bustin, Aurelien, Qi, Haikun, Hajhosseiny, Reza, Masci, Pier Giorgio, Neji, Radhouene, Rueckert, Daniel, Botnar, René M., Prieto, Claudia

“…Cardiac CINE magnetic resonance imaging is the gold-standard for the assessment of cardiac function. Imaging accelerations have shown to enable 3D CINE with…”
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Rapid mono and biexponential 3D-T1ρ mapping of knee cartilage using variational networks by Zibetti, Marcelo V. W., Johnson, Patricia M., Sharafi, Azadeh, Hammernik, Kerstin, Knoll, Florian, Regatte, Ravinder R.

“…In this study we use undersampled MRI acquisition methods to obtain accelerated 3D mono and biexponential spin–lattice relaxation time in the rotating frame (T…”
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CG‐SENSE revisited: Results from the first ISMRM reproducibility challenge by Maier, Oliver, Baete, Steven Hubert, Fyrdahl, Alexander, Hammernik, Kerstin, Harrevelt, Seb, Kasper, Lars, Karakuzu, Agah, Loecher, Michael, Patzig, Franz, Tian, Ye, Wang, Ke, Gallichan, Daniel, Uecker, Martin, Knoll, Florian

“…Purpose The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to…”
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Cardiac MR: From Theory to Practice by Ismail, Tevfik F, Strugnell, Wendy, Coletti, Chiara, Božić-Iven, Maša, Weingärtner, Sebastian, Hammernik, Kerstin, Correia, Teresa, Küstner, Thomas

“…Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs…”
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Self-Supervised Motion-Corrected Image Reconstruction Network for 4D Magnetic Resonance Imaging of the Body Trunk by Küstner, Thomas, Pan, Jiazhen, Gilliam, Christopher, Qi, Haikun, Cruz, Gastao, Hammernik, Kerstin, Blu, Thierry, Rueckert, Daniel, Botnar, René, Prieto, Claudia, Gatidis, Sergios

“…Respiratory motion can cause artifacts in magnetic resonance imaging of the body trunk if patients cannot hold their breath or triggered acquisitions are not…”
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Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity‐weighted coil combination by Hammernik, Kerstin, Schlemper, Jo, Qin, Chen, Duan, Jinming, Summers, Ronald M., Rueckert, Daniel

“…Purpose To systematically investigate the influence of various data consistency layers and regularization networks with respect to variations in the training…”
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Predictive uncertainty in deep learning-based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data set by Küstner, Thomas, Hammernik, Kerstin, Rueckert, Daniel, Hepp, Tobias, Gatidis, Sergios

“…To estimate pixel-wise predictive uncertainty for deep learning-based MR image reconstruction and to examine the impact of domain shifts and architecture…”
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Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction by Narnhofer, Dominik, Effland, Alexander, Kobler, Erich, Hammernik, Kerstin, Knoll, Florian, Pock, Thomas

“…Recent deep learning approaches focus on improving quantitative scores of dedicated benchmarks, and therefore only reduce the observation-related (aleatoric)…”
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Motion-Compensated MR CINE Reconstruction With Reconstruction-Driven Motion Estimation by Pan, Jiazhen, Huang, Wenqi, Rueckert, Daniel, Kustner, Thomas, Hammernik, Kerstin

“…In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach to address highly undersampled acquisitions by incorporating motion…”
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Unrolled and rapid motion-compensated reconstruction for cardiac CINE MRI by Pan, Jiazhen, Hamdi, Manal, Huang, Wenqi, Hammernik, Kerstin, Kuestner, Thomas, Rueckert, Daniel

“…In recent years Motion-Compensated MR reconstruction (MCMR) has emerged as a promising approach for cardiac MR (CMR) imaging reconstruction. MCMR estimates…”
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Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review by Spieker, Veronika, Eichhorn, Hannah, Hammernik, Kerstin, Rueckert, Daniel, Preibisch, Christine, Karampinos, Dimitrios C., Schnabel, Julia A.

“…Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged…”
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Single‐heartbeat cardiac cine imaging via jointly regularized nonrigid motion‐corrected reconstruction by Cruz, Gastao, Hammernik, Kerstin, Kuestner, Thomas, Velasco, Carlos, Hua, Alina, Ismail, Tevfik Fehmi, Rueckert, Daniel, Botnar, Rene Michael, Prieto, Claudia

“…The aim of the current study was to develop a novel approach for 2D breath‐hold cardiac cine imaging from a single heartbeat, by combining cardiac…”
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Deep learning-based whole-brain B1 +-mapping at 7T by Krueger, Felix, Aigner, Christoph Stefan, Lutz, Max, Riemann, Layla Tabea, Degenhardt, Katja, Hadjikiriakos, Kimon, Zimmermann, Felix Frederik, Hammernik, Kerstin, Schulz-Menger, Jeanette, Schaeffter, Tobias, Schmitter, Sebastian

“…This study investigates the feasibility of using complex-valued neural networks (NNs) to estimate quantitative transmit magnetic RF field (B1 +) maps from…”
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Attention-Aware Non-Rigid Image Registration for Accelerated MR Imaging by Ghoul, Aya, Pan, Jiazhen, Lingg, Andreas, Kubler, Jens, Krumm, Patrick, Hammernik, Kerstin, Rueckert, Daniel, Gatidis, Sergios, Kustner, Thomas

“…Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without…”
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Rapid estimation of 2D relative B1+‐maps from localizers in the human heart at 7T using deep learning by Krueger, Felix, Aigner, Christoph Stefan, Hammernik, Kerstin, Dietrich, Sebastian, Lutz, Max, Schulz‐Menger, Jeanette, Schaeffter, Tobias, Schmitter, Sebastian

“…Purpose Subject‐tailored parallel transmission pulses for ultra‐high fields body applications are typically calculated based on subject‐specific B1+$$…”
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Complementary time‐frequency domain networks for dynamic parallel MR image reconstruction by Qin, Chen, Duan, Jinming, Hammernik, Kerstin, Schlemper, Jo, Küstner, Thomas, Botnar, René, Prieto, Claudia, Price, Anthony N., Hajnal, Joseph V., Rueckert, Daniel

“…Purpose To introduce a novel deep learning‐based approach for fast and high‐quality dynamic multicoil MR reconstruction by learning a complementary…”
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Deep learning-based whole-brain B 1 + -mapping at 7T by Krueger, Felix, Aigner, Christoph Stefan, Lutz, Max, Riemann, Layla Tabea, Degenhardt, Katja, Hadjikiriakos, Kimon, Zimmermann, Felix Frederik, Hammernik, Kerstin, Schulz-Menger, Jeanette, Schaeffter, Tobias, Schmitter, Sebastian

“…This study investigates the feasibility of using complex-valued neural networks (NNs) to estimate quantitative transmit magnetic RF field (B ) maps from…”
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A Deep Learning-Based Integrated Framework for Quality-Aware Undersampled Cine Cardiac MRI Reconstruction and Analysis by Machado, Ines, Puyol-Anton, Esther, Hammernik, Kerstin, Cruz, Gastao, Ugurlu, Devran, Olakorede, Ihsane, Oksuz, Ilkay, Ruijsink, Bram, Castelo-Branco, Miguel, Young, Alistair, Prieto, Claudia, Schnabel, Julia, King, Andrew

“…Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow…”
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