Daniel K. Sodickson
New York University
273 Papers
4.3K Citations
Daniel K. Sodickson is an academic researcher from New York University. The author has contributed to research in topics: Iterative reconstruction & Electromagnetic coil. The author has an hindex of 61, co-authored 258 publications. Previous affiliations of Daniel K. Sodickson include Harvard University & Beth Israel Deaconess Medical Center.
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Papers
Simultaneous acquisition of spatial harmonics (SMASH): ultra-fast imaging with radiofrequency coil arrays
TL;DR: SiMultaneous Acquisition of Spatial Harmonics (SMASH) as mentioned in this paper is a partially parallel imaging strategy, which is readily integrated with many existing fast imaging sequences, yielding multiplicative time savings without a significant sacrifice in spatial resolution or signal-to-noise ratio.
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Learning a variational network for reconstruction of accelerated MRI data.
Kerstin Hammernik,Teresa Klatzer,Erich Kobler,Michael P. Recht,Daniel K. Sodickson,Thomas Pock,Thomas Pock,Florian Knoll +7 more
TL;DR: In this paper, a variational network approach is proposed to reconstruct the clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data.
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Learning a Variational Network for Reconstruction of Accelerated MRI Data
Kerstin Hammernik,Teresa Klatzer,Erich Kobler,Michael P. Recht,Daniel K. Sodickson,Thomas Pock,Thomas Pock,Florian Knoll +7 more
TL;DR: To allow fast and high‐quality reconstruction of clinical accelerated multi‐coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning.
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fastMRI: An Open Dataset and Benchmarks for Accelerated MRI.
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 James Owens,C. Lawrence Zitnick,Michael P. Recht,Daniel K. Sodickson,Yvonne W. Lui +22 more
TL;DR: The fastMRI dataset is introduced, a large-scale collection of both raw MR measurements and clinical MR images that can be used for training and evaluation of machine-learning approaches to MR image reconstruction.
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Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components.
TL;DR: The low‐rank plus sparse (L+S) matrix decomposition model is applied to reconstruct undersampled dynamic MRI as a superposition of background and dynamic components in various problems of clinical interest.
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