World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
5396
World Ranking
12573
National Ranking
795

Andrew J. Reader 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 Andrew J. Reader 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: 245 publications — 61st percentile

61% of scientists in this discipline score the same or lower.

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

Andrew J. Reader 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 Andrew J. Reader 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Andrew J. Reader is affiliated with King's College London in the United Kingdom. Their research primarily focuses on the intersection of medicine and engineering, with a strong emphasis on medical imaging and related technologies.

Their main fields of study include:

  • Medicine
  • Engineering

Within these disciplines, their work concentrates on several subfields such as:

  • Radiology, Nuclear Medicine and Imaging
  • Biomedical Engineering
  • Radiation
  • Molecular Biology
  • Pulmonary and Respiratory Medicine

The key topics covered in their body of work include:

  • Medical Imaging Techniques and Applications
  • Advanced MRI Techniques and Applications
  • Radiomics and Machine Learning in Medical Imaging
  • Advanced X-ray and CT Imaging
  • Radiation Detection and Scintillator Technologies
  • Nuclear Physics and Applications
  • Advanced Radiotherapy Techniques

Andrew J. Reader has contributed to various publications, with a notable number appearing in the following frequent venues:

  • IEEE Transactions on Radiation and Plasma Medical Sciences
  • 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
  • arXiv (Cornell University)
  • Radiological Physics and Technology
  • Medical Physics

Their recent published papers include:

  • Deep Learning for PET Image Reconstruction, 2020, IEEE Transactions on Radiation and Plasma Medical Sciences
  • AI for PET image reconstruction, 2023, British Journal of Radiology
  • Deep learning-based PET image denoising and reconstruction: a review, 2024, Radiological Physics and Technology

Andrew J. Reader frequently collaborates with several co-authors across their research projects. Notable collaborators include:

  • Alexander Hammers
  • Paul Marsden
  • Radhouène Neji
  • Andrew P. King
  • Sam Ellis

The researcher's contributions span significant advancements in positron emission tomography (PET) image reconstruction methods, particularly leveraging deep learning techniques and machine learning models. This work supports enhanced imaging quality and robustness in medical scanning technologies.

Best Publications

  • List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations

    F Lamare;F Lamare;M J Ledesma Carbayo;T Cresson;G Kontaxakis

  • Impact of image-space resolution modeling for studies with the high-resolution research tomograph.

    Florent C. Sureau;Andrew J. Reader;Claude Comtat;Claire Leroy

  • One-pass list-mode EM algorithm for high-resolution 3-D PET image reconstruction into large arrays

    A.J. Reader;S. Ally;F. Bakatselos;R. Manavaki

  • EM algorithm system modeling by image-space techniques for PET reconstruction

    A.J. Reader;P.J. Julyan;H. Williams;D.L. Hastings

  • Fast accurate iterative reconstruction for low-statistics positron volume imaging

    Andrew Reader;K. Erlandsson;M. A. Flower;R. J. Ott

  • Deep Learning for PET Image Reconstruction

    Andrew J. Reader;Guillaume Corda;Abolfazl Mehranian;Casper da Costa-Luis

  • Advances in PET Image Reconstruction

    Andrew J. Reader;Habib Zaidi

  • Where in-vivo imaging meets cytoarchitectonics: The relationship between cortical thickness and neuronal density measured with high-resolution [18F]flumazenil-PET

    Christian la Fougère;Sarah Grant;Alexey Kostikov;Ralf Schirrmacher

  • Respiratory motion correction for PET oncology applications using affine transformation of list mode data

    F Lamare;T Cresson;J Savean;C Cheze Le Rest

  • Performance Evaluation of the 32-Module quadHIDAC Small-Animal PET Scanner

    Klaus P. Schäfers;Andrew J. Reader;Michael Kriens;Christof Knoess

  • Statistical list-mode image reconstruction for the high resolution research tomograph

    A. Rahmim;M. Lenox;Andrew J. Reader;Christian Michel

  • Joint estimation of dynamic PET images and temporal basis functions using fully 4D ML-EM

    Andrew J Reader;Florent C Sureau;Claude Comtat;Régine Trébossen

  • 4D image reconstruction for emission tomography

    Andrew J Reader;Jeroen Verhaeghe

  • Model-Based Deep Learning PET Image Reconstruction Using Forward–Backward Splitting Expectation–Maximization

    Abolfazl Mehranian;Andrew J. Reader

  • Fast ray-tracing technique to calculate line integral paths in voxel arrays

    Huaxia Zhao;A.J. Reader

  • Characterization of age/sex and the regional distribution of mGluR5 availability in the healthy human brain measured by high-resolution [(11)C]ABP688 PET.

    Jonathan M. DuBois;Olivier G. Rousset;Jared Rowley;Manuel Porras-Betancourt

  • MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions

    Philip Novosad;Andrew J Reader;Andrew J Reader

  • Fully 4D image reconstruction by estimation of an input function and spectral coefficients

    A. J. Reader;J.C. Matthews;F.C. Sureau;C. Comtat

  • Regularized one-pass list-mode EM algorithm for high resolution 3D PET image reconstruction into large arrays

    A.J. Reader;S. Ally;F. Bakatselos;R. Manavaki

  • Intercomparison of four reconstruction techniques for positron volume imaging with rotating planar detectors

    Andrew Reader;D. Visvikis;K. Erlandsson;R. J. Ott

  • Direct reconstruction of parametric images using any spatiotemporal 4D image based model and maximum likelihood expectation maximisation

    Julian C. Matthews;Georgios I. Angelis;Fotis A. Kotasidis;Pawel J. Markiewicz

  • MR-Guided Kernel EM Reconstruction for Reduced Dose PET Imaging

    James Bland;Abolfazl Mehranian;Martin A. Belzunce;Sam Ellis

Frequent Co-Authors

Claudia Prieto
Claudia Prieto Pontificia Universidad Católica de Chile
William R B Lionheart
William R B Lionheart University of Manchester
Alexander Hammers
Alexander Hammers King's College London
Jean-Paul Soucy
Jean-Paul Soucy Montreal Neurological Institute and Hospital
Andrew P. King
Andrew P. King King's College London
Georg Northoff
Georg Northoff University of Ottawa
Dimitris Visvikis
Dimitris Visvikis University of Western Brittany
Pedro Rosa-Neto
Pedro Rosa-Neto McGill University
Alain Dagher
Alain Dagher Montreal Neurological Institute and Hospital
Marco Leyton
Marco Leyton McGill University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online degrees is a smart way to accelerate your career in computer science. Many students are now choosing flexible learning options that fit their busy schedules and budgets. For those eager to advance quickly, consider the shortest masters degree programs online to fast-track your credentials without compromising on quality.

If you’re aiming to boost employability, look into which master's degree is most in demand in usa. Programs in data science, artificial intelligence, and cybersecurity often lead to high-demand roles and competitive salaries.

For those starting out or seeking a more affordable entry into the field, an online associate degree in computer science delivers essential skills and can be completed in less time than a bachelor’s. These credentials can open doors to entry-level IT jobs or further study.

Cost is another major consideration. To control expenses while advancing your education, be sure to research cheap online degrees fast to find reputable programs that offer solid value for your investment.

Best Scientists Citing Andrew J. Reader

Trending Scientists

Recently Published Articles