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
XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing.
TL;DR: A novel framework for free‐breathing MRI is developed called XD‐GRASP, which sorts dynamic data into extra motion‐state dimensions using the self‐navigation properties of radial imaging and reconstructs the multidimensional dataset using compressed sensing.
Posted Content
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.
Journal ArticleDOI
Comprehensive quantification of signal-to-noise ratio and g-factor for image-based and k-space-based parallel imaging reconstructions.
Philip M. Robson,Aaron K. Grant,Ananth J. Madhuranthakam,Riccardo Lattanzi,Daniel K. Sodickson,Charles A. McKenzie +5 more
TL;DR: A simple Monte Carlo based method is proposed for all linear image reconstruction algorithms, which allows measurement of signal‐to‐noise ratio and g‐factor and is demonstrated for SENSE and GRAPPA reconstructions for accelerated acquisitions that have not previously been amenable to such assessment.
Posted Content
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.
Journal ArticleDOI
Double-oblique free-breathing high resolution three-dimensional coronary magnetic resonance angiography
Matthias Stuber,Matthias Stuber,René M. Botnar,René M. Botnar,Peter G. Danias,Daniel K. Sodickson,Kraig V. Kissinger,Marc Van Cauteren,Jan De Becker,Warren J. Manning +9 more
TL;DR: Double-oblique submillimeter free-breathing coronary MRA allows depiction of extensive parts of the native coronary arteries and has the potential to be applied in broader prospective multicenter studies where coronary Mra is compared with X-ray angiography.