World's Best Scientists 2026 revealed!
Daniel K. Sodickson

Daniel K. Sodickson

D-Index & Metrics

Engineering and Technology

D-Index
74
Citations
23976
World Ranking
801
National Ranking
279

Daniel K. Sodickson publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Daniel K. Sodickson sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 272 publications — 70th percentile

70% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Daniel K. Sodickson D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Daniel K. Sodickson sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 74 D-Index — 92nd percentile

92% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Research.com Recognitions

  • 2020 - Fellow, National Academy of Inventors

Overview

Daniel K. Sodickson is affiliated with New York University in the United States. Their primary field of study is Medicine, with a particular focus on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering, Atomic and Molecular Physics, and Optics. Additional subfields include Pulmonary and Respiratory Medicine and Artificial Intelligence.

The research topics covered in their work include:

  • Advanced MRI Techniques and Applications
  • Medical Imaging Techniques and Applications
  • Radiomics and Machine Learning in Medical Imaging
  • Atomic and Subatomic Physics Research
  • MRI in cancer diagnosis
  • Prostate Cancer Diagnosis and Treatment
  • Advanced X-ray and CT Imaging

Among the recent papers authored or co-authored by Daniel K. Sodickson are:

  • fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning (2020, Radiology Artificial Intelligence)
  • Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues (2020, IEEE Signal Processing Magazine)
  • Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge (2020, Magnetic Resonance in Medicine)
  • Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study (2020, American Journal of Roentgenology)
  • Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI (2023, Radiology)

Frequently collaborating co-authors include:

  • Hersh Chandarana
  • Sumit Chopra
  • Patricia M. Johnson
  • Angela Tong
  • Tarun Dutt

The most common venues for publications by Daniel K. Sodickson are:

  • Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
  • Magnetic Resonance in Medicine
  • arXiv (Cornell University)
  • Radiology Artificial Intelligence
  • IEEE Signal Processing Magazine

Daniel K. Sodickson received the award of Fellow from the National Academy of Inventors in 2020.

Best Publications

  • Simultaneous acquisition of spatial harmonics (SMASH): ultra-fast imaging with radiofrequency coil arrays

    Daniel Kevin Sodickson

  • Learning a variational network for reconstruction of accelerated MRI data.

    Kerstin Hammernik;Teresa Klatzer;Erich Kobler;Michael P. Recht

  • 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

  • Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components.

    Ricardo Otazo;Emmanuel Candès;Daniel K. Sodickson

  • XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing.

    Li Feng;Leon Axel;Hersh Chandarana;Kai Tobias Block

  • fastMRI: An Open Dataset and Benchmarks for Accelerated MRI.

    Jure Zbontar;Florian Knoll;Anuroop Sriram;Matthew J. Muckley

  • Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI.

    Ricardo Otazo;Daniel Kim;Leon Axel;Daniel K. Sodickson

  • 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

  • AUTO-SMASH: A self-calibrating technique for SMASH imaging

    Peter M. Jakob;Mark A. Griswold;Robert R. Edelman;Daniel K. Sodickson

  • 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

  • fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning.

    Florian Knoll;Jure Zbontar;Anuroop Sriram;Matthew J Muckley

  • Default-Mode Network Disruption in Mild Traumatic Brain Injury

    Yongxia Zhou;Michael P. Milham;Yvonne W. Lui;Laura Miles

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

    Florian Knoll;Kerstin Hammernik;Chi Zhang;Steen Moeller

  • An introduction to coil array design for parallel MRI.

    Michael A. Ohliger;Michael A. Ohliger;Daniel K. Sodickson

  • A generalized approach to parallel magnetic resonance imaging.

    Daniel K. Sodickson;Charles A. McKenzie

  • AUTO-SMASH: a self-calibrating technique for SMASH imaging. SiMultaneous Acquisition of Spatial Harmonics.

    P M Jakob;M A Griswold;R R Edelman;D K Sodickson

  • Compressed sensing for body MRI

    Li Feng;Thomas Benkert;Kai Tobias Block;Daniel K. Sodickson

  • Ultimate intrinsic signal-to-noise ratio for parallel MRI: electromagnetic field considerations.

    Michael A. Ohliger;Aaron K. Grant;Daniel K. Sodickson;Daniel K. Sodickson

  • Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer.

    E. E. Sigmund;G. Y. Cho;S. Kim;M. Finn

  • Highly accelerated real-time cardiac cine MRI using k–t SPARSE-SENSE

    Li Feng;Monvadi B. Srichai;Ruth P. Lim;Alexis Harrison

  • Free-Breathing Contrast-Enhanced Multiphase MRI of the Liver Using a Combination of Compressed Sensing, Parallel Imaging, and Golden-Angle Radial Sampling

    Hersh Chandarana;Li Feng;Tobias K. Block;Andrew B. Rosenkrantz

  • Golden-Angle Radial Sparse Parallel MRI: Combination of Compressed Sensing, Parallel Imaging, and Golden-Angle Radial Sampling for Fast and Flexible

    Li Feng;Robert Grimm;Kai Tobias Block;Hersh Chandarana

Frequent Co-Authors

Ricardo Otazo
Ricardo Otazo Memorial Sloan Kettering Cancer Center
Florian Knoll
Florian Knoll University of Erlangen-Nuremberg
Thomas Pock
Thomas Pock Graz University of Technology
Matthias Stuber
Matthias Stuber University of Lausanne
Christopher M. Collins
Christopher M. Collins New York University
Robert R. Edelman
Robert R. Edelman Northwestern University
James S. Babb
James S. Babb New York University
Warren J. Manning
Warren J. Manning Beth Israel Deaconess Medical Center
Emmanuel J. Candès
Emmanuel J. Candès Stanford University
Mark A. Griswold
Mark A. Griswold Case Western Reserve University

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