| Learning a variational network for reconstruction of accelerated MRI data K Hammernik, T Klatzer, E Kobler, MP Recht, DK Sodickson, T Pock, ... Magnetic resonance in medicine 79 (6), 3055-3071, 2018 | 950 | 2018 |
| Second order total generalized variation (TGV) for MRI F Knoll, K Bredies, T Pock, R Stollberger Magnetic resonance in medicine 65 (2), 480-491, 2011 | 552 | 2011 |
| fastMRI: An open dataset and benchmarks for accelerated MRI J Zbontar, F Knoll, A Sriram, T Murrell, Z Huang, MJ Muckley, A Defazio, ... arXiv preprint arXiv:1811.08839, 2018 | 337 | 2018 |
| Multiparametric imaging with heterogeneous radiofrequency fields MA Cloos, F Knoll, T Zhao, KT Block, M Bruno, GC Wiggins, ... Nature communications 7 (1), 1-10, 2016 | 187 | 2016 |
| Low rank alternating direction method of multipliers reconstruction for MR fingerprinting J Assländer, MA Cloos, F Knoll, DK Sodickson, J Hennig, R Lattanzi Magnetic resonance in medicine 79 (1), 83-96, 2018 | 163 | 2018 |
| Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues F Knoll, K Hammernik, C Zhang, S Moeller, T Pock, DK Sodickson, ... IEEE signal processing magazine 37 (1), 128-140, 2020 | 142* | 2020 |
| Assessment of the generalization of learned image reconstruction and the potential for transfer learning F Knoll, K Hammernik, E Kobler, T Pock, MP Recht, DK Sodickson Magnetic resonance in medicine 81 (1), 116-128, 2019 | 132 | 2019 |
| Parallel imaging with nonlinear reconstruction using variational penalties F Knoll, C Clason, K Bredies, M Uecker, R Stollberger Magnetic resonance in medicine 67 (1), 34-41, 2012 | 124 | 2012 |
| Total generalized variation in diffusion tensor imaging T Valkonen, K Bredies, F Knoll SIAM Journal on Imaging Sciences 6 (1), 487-525, 2013 | 110 | 2013 |
| fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning F Knoll, J Zbontar, A Sriram, MJ Muckley, M Bruno, A Defazio, M Parente, ... Radiology: Artificial Intelligence 2 (1), e190007, 2020 | 107 | 2020 |
| Joint MR-PET reconstruction using a multi-channel image regularizer F Knoll, M Holler, T Koesters, R Otazo, K Bredies, DK Sodickson IEEE transactions on medical imaging 36 (1), 1-16, 2016 | 103 | 2016 |
| Gibbs ringing in diffusion MRI J Veraart, E Fieremans, IO Jelescu, F Knoll, DS Novikov Magnetic resonance in medicine 76 (1), 301-314, 2016 | 100 | 2016 |
| Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge F Knoll, T Murrell, A Sriram, N Yakubova, J Zbontar, M Rabbat, A Defazio, ... Magnetic resonance in medicine 84 (6), 3054-3070, 2020 | 84 | 2020 |
| Adapted random sampling patterns for accelerated MRI F Knoll, C Clason, C Diwoky, R Stollberger Magnetic resonance materials in physics, biology and medicine 24 (1), 43-50, 2011 | 82 | 2011 |
| gpuNUFFT-an open source GPU library for 3D regridding with direct Matlab interface F Knoll, A Schwarzl, C Diwoky, DK Sodickson Proceedings of the 22nd annual meeting of ISMRM, Milan, Italy, 4297, 2014 | 79 | 2014 |
| Results of the 2020 fastmri challenge for machine learning mr image reconstruction MJ Muckley, B Riemenschneider, A Radmanesh, S Kim, G Jeong, J Ko, ... IEEE transactions on medical imaging 40 (9), 2306-2317, 2021 | 65* | 2021 |
| End-to-end variational networks for accelerated MRI reconstruction A Sriram, J Zbontar, T Murrell, A Defazio, CL Zitnick, N Yakubova, F Knoll, ... International Conference on Medical Image Computing and Computer-Assisted …, 2020 | 64 | 2020 |
| Artificial intelligence in musculoskeletal imaging: current status and future directions S Gyftopoulos, D Lin, F Knoll, AM Doshi, TC Rodrigues, MP Recht AJR. American journal of roentgenology 213 (3), 506, 2019 | 62 | 2019 |
| Fast T2 mapping with improved accuracy using undersampled spin-echo MRI and model-based reconstructions with a generating function TJ Sumpf, A Petrovic, M Uecker, F Knoll, J Frahm IEEE transactions on medical imaging 33 (12), 2213-2222, 2014 | 60 | 2014 |
| Using deep learning to accelerate knee MRI at 3 T: results of an interchangeability study MP Recht, J Zbontar, DK Sodickson, F Knoll, N Yakubova, A Sriram, ... AJR. American journal of roentgenology 215 (6), 1421, 2020 | 47 | 2020 |
Daniel SodicksonVice-Chair for Research, Department of Radiology, New York University School of MedicineVerified email at med.nyu.edu
