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Daniel K. Sodickson
Researcher at New York University
Publications - 267
Citations - 18645
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 receiving 15371 citations. Previous affiliations of Daniel K. Sodickson include Harvard University & Beth Israel Deaconess Medical Center.
Papers
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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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI
Li Feng,Robert Grimm,Kai Tobias Block,Hersh Chandarana,Sungheon Kim,Jian Xu,Leon Axel,Daniel K. Sodickson,Ricardo Otazo +8 more
TL;DR: To develop a fast and flexible free‐breathing dynamic volumetric MRI technique, iterative Golden‐angle RAdial Sparse Parallel MRI (iGRASP), that combines compressed sensing, parallel imaging, and golden‐angle radial sampling.
Journal ArticleDOI
Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI.
TL;DR: Comp compressed sensing and parallel imaging are combined by merging the k‐t SPARSE technique with sensitivity encoding (SENSE) reconstruction to substantially increase the acceleration rate for perfusion imaging and a new theoretical framework is presented for understanding the combination of k-t SParSE with SENSE based on distributed compressed sensing theory.