World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
8117
World Ranking
11042
National Ranking
551

Florian Knoll publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Florian Knoll sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 166 publications — 33rd percentile

33% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Florian Knoll D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Florian Knoll sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 36 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Florian Knoll is affiliated with the University of Erlangen-Nuremberg in Germany. Their research primarily focuses on advanced techniques in magnetic resonance imaging (MRI), combining expertise in medicine and biomedical engineering with an emphasis on machine learning applications within medical imaging.

Their scholarly contributions include recent papers such as:

  • 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
  • Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction, 2021, IEEE Transactions on Medical Imaging
  • Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians, 2020, Journal of Magnetic Resonance Imaging
  • Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge, 2020, Magnetic Resonance in Medicine

Frequent coauthors in Florian Knoll's research include:

  • Patricia M. Johnson
  • Zhengguo Tan
  • Zhengnan Huang
  • Kerstin Hammernik
  • Frederik B. Laun

They publish regularly in prominent venues such as:

  • 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 (22 publications)
  • arXiv (Cornell University) (10 publications)
  • Zenodo (CERN European Organization for Nuclear Research) (6 publications)
  • Magnetic Resonance in Medicine (5 publications)
  • IEEE Signal Processing Magazine (4 publications)

Their main field of study is medicine, with a particular specialization in radiology, nuclear medicine, and imaging, accounting for the majority of their research output.

Additional subfields include:

  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Atomic and Molecular Physics, and Optics
  • Pulmonary and Respiratory Medicine

The main topics covered in their work focus strongly on advanced MRI techniques and applications, medical imaging techniques, and radiomics with machine learning approaches in medical imaging. Other notable thematic areas include MRI in cancer diagnosis, advanced X-ray and CT imaging, neuroimaging techniques, and cardiac imaging and diagnostics.

Overall, Florian Knoll's research integrates machine learning methods with MRI technology to address challenges in medical imaging reconstruction, contributing to datasets, survey papers, and challenge benchmarks in the field.

Best Publications

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

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

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

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

  • Second order total generalized variation (TGV) for MRI

    Florian Knoll;Kristian Bredies;Thomas Pock;Rudolf Stollberger

  • 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

  • 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

  • Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

    Matthew J. Muckley;Bruno Riemenschneider;Alireza Radmanesh;Sunwoo Kim

  • Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.

    Dana J. Lin;Patricia M. Johnson;Florian Knoll;Yvonne W. Lui

  • Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

    Florian Knoll;Tullie Murrell;Anuroop Sriram;Nafissa Yakubova

  • Assessment of the generalization of learned image reconstruction and the potential for transfer learning

    Florian Knoll;Kerstin Hammernik;Kerstin Hammernik;Erich Kobler;Thomas Pock;Thomas Pock

  • Multiparametric imaging with heterogeneous radiofrequency fields.

    Martijn A. Cloos;Florian Knoll;Tiejun Zhao;Tiejun Zhao;Kai T. Block

  • End-to-End Variational Networks for Accelerated MRI Reconstruction

    Anuroop Sriram;Jure Zbontar;Tullie Murrell;Aaron Defazio

  • Low Rank Alternating Direction Method of Multipliers Reconstruction for MR Fingerprinting

    Jakob Assländer;Martijn A. Cloos;Florian Knoll;Daniel K. Sodickson

  • Gibbs ringing in diffusion MRI

    Jelle Veraart;Els Fieremans;Ileana O. Jelescu;Florian Knoll

  • Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

    Florian Knoll;Tullie Murrell;Anuroop Sriram;Nafissa Yakubova

  • Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions

    Soterios Gyftopoulos;Dana Lin;Florian Knoll;Ankur M. Doshi

  • Total Generalized Variation in Diffusion Tensor Imaging

    Tuomo Valkonen;Kristian Bredies;Florian Knoll

  • Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study.

    Michael P. Recht;Jure Zbontar;Daniel K. Sodickson;Florian Knoll

  • Parallel Imaging with Nonlinear Reconstruction using Variational Penalties

    Florian Knoll;Christian Clason;Kristian Bredies;Martin Uecker

  • Joint MR-PET Reconstruction Using a Multi-Channel Image Regularizer

    Florian Knoll;Martin Holler;Thomas Koesters;Ricardo Otazo

  • Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI.

    Unknown

  • Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging

    Unknown

  • Adapted Random Sampling Patterns for Accelerated MRI

    Florian Knoll;Christian Clason;Clemens Diwoky;Rudolf Stollberger

  • Low Rank Alternating Direction Method of Multipliers Reconstruction for MR Fingerprinting

    Jakob Assländer;Martijn A Cloos;Florian Knoll;Daniel K Sodickson

  • Assessment of the generalization of learned image reconstruction and the potential for transfer learning

    Florian Knoll;Kerstin Hammernik;Thomas Pock;Daniel K Sodickson

Frequent Co-Authors

Daniel K. Sodickson
Daniel K. Sodickson New York University
Thomas Pock
Thomas Pock Graz University of Technology
Kristian Bredies
Kristian Bredies University of Graz
Ricardo Otazo
Ricardo Otazo Memorial Sloan Kettering Cancer Center
C. Lawrence Zitnick
C. Lawrence Zitnick Facebook (United States)
Florian Kronenberg
Florian Kronenberg Innsbruck Medical University
Michael Rabbat
Michael Rabbat Facebook (United States)
Michael Hintermüller
Michael Hintermüller Weierstrass Institute for Applied Analysis and Stochastics
Jean-Luc Starck
Jean-Luc Starck University of Paris-Saclay
Günter Weiss
Günter Weiss Innsbruck Medical University

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