D
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
More filters
Journal ArticleDOI
Joint reconstruction of simultaneously acquired MR-PET data with multi sensor compressed sensing based on a joint sparsity constraint
Florian Knoll,Thomas Koesters,Ricardo Otazo,Tobias K. Block,Li Feng,Kathleen Vunckx,David Faul,Johan Nuyts,Fernando E. Boada,Daniel K. Sodickson +9 more
TL;DR: A new iterative joint reconstruction framework based on multi-sensor compressed sensing that exploits anatomical correlations between MR and PET that provides additional enhancements to the information content of multimodality studies is proposed.
Journal ArticleDOI
The brain after COVID-19: Compensatory neurogenesis or persistent neuroinflammation?
Journal ArticleDOI
Approaching ultimate intrinsic specific absorption rate in radiofrequency shimming using high-permittivity materials at 7 Tesla.
Gillian G. Haemer,Manushka Vaidya,Christopher M. Collins,Daniel K. Sodickson,Graham C. Wiggins,Riccardo Lattanzi +5 more
TL;DR: The aim of this study was to evaluate the effect of integrated high‐permittivity materials (HPMs) on excitation homogeneity and global specific absorption rate (SAR) for transmit arrays at 7T.
Journal ArticleDOI
Hybrid T2 - and T1 -weighted radial acquisition for free-breathing abdominal examination.
Thomas Benkert,John P. Mugler,David Rigie,Daniel K. Sodickson,Hersh Chandarana,Kai Tobias Block +5 more
TL;DR: A hybrid imaging approach is described that creates T2‐ and T1‐weighted images from a single scan and allows for free‐breathing acquisition.
Journal ArticleDOI
Optimized Quantification of Spin Relaxation Times in the Hybrid State
TL;DR: In this article, the analysis of optimized spin ensemble trajectories for relaxometry in the hybrid state was performed, and numerical optimizations were performed to find spin trajectories that minimize the Cramer-Rao bound for $T_1$-encoding, and their weighted sum, respectively, followed by a comparison with the optimized spin-trajectory, as well as Look-Locker and multi-spin-echo methods.