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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.

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Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues

TL;DR: An overview of the recent machine-learning approaches that have been proposed specifically for improving parallel imaging is provided and a general background introduction to parallel MRI is given and structured around the classical view of image- and k-space-based methods.
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Compressed sensing for body MRI

TL;DR: An overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution, is presented.
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Thalamic Resting-State Functional Networks: Disruption in Patients with Mild Traumatic Brain Injury

TL;DR: Results lend further support to the presumed subtle thalamic injury in patients with MTBI and suggest resting-state functional MR imaging can be used as an additional imaging modality for detection of thalamocortical connectivity abnormalities and for better understanding of the complex persistent postconcussive syndrome.
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Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer.

TL;DR: The results suggest the potential of intravoxel incoherent motion vascular and cellular biomarkers for initial grading, progression monitoring, or treatment assessment of breast tumors.
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Rapid and accurate T2 mapping from multi-spin-echo data using Bloch-simulation-based reconstruction.

TL;DR: This work presents a novel postprocessing approach aiming to overcome the common penalties associated with multiecho protocols, and enabling rapid and accurate mapping of T2 relaxation values.