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Xiaobo Qu

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Computer Science 61 3129 3038 421 418 248 11433

Xiaobo Qu publications per year

The chart shows the history of publications by Xiaobo Qu between 2007 and 2025, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Xiaobo Qu published across 19 years, from 2007 to 2025, averaging 22 papers a year. Output peaked at 57 publications in 2021. 73 of the 418 publications appeared in the last two years.

No. of publications
10 20 30 40 50
Bar chart. Horizontal axis: year, 2007 to 2025. Vertical axis: number of publications, 0 to 57. Peak 57 publications in 2021. 2007: 4 publications 2008: 4 publications 2009: 4 publications 2010: 6 publications 2011: 12 publications 2012: 12 publications 2013: 10 publications 2014: 24 publications 2015: 33 publications 2016: 13 publications 2017: 22 publications 2018: 25 publications 2019: 24 publications 2020: 44 publications 2021: 57 publications 2022: 21 publications 2023: 30 publications 2024: 30 publications 2025: 43 publications
2007 2025

418 publications in total across all disciplines

View publications per year as a table
Xiaobo Qu: publications per year, 2007 to 2025
Year Publications
2007 4
2008 4
2009 4
2010 6
2011 12
2012 12
2013 10
2014 24
2015 33
2016 13
2017 22
2018 25
2019 24
2020 44
2021 57
2022 21
2023 30
2024 30
2025 43
Total 418
Download as CSV

Xiaobo Qu 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 Xiaobo Qu sits on this spectrum.

No. of scientists
200 400 600
Bar chart with 97 bars. Horizontal axis: publications, 32–41 to 991+. Vertical axis: number of scientists, 0 to 609. Most scientists, 609, have 142–151 publications. The last bar groups every scientist with 991 publications or more. The highlighted bar, 242–251 publications, is where this scientist sits. 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
32–41 publications 991+

This scientist: 248 publications — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

View publications distribution as a table
Number of Computer Science scientists by publication count, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
Publications Scientists This scientist
32–41 7
42–51 22
52–61 82
62–71 134
72–81 249
82–91 324
92–101 421
102–111 420
112–121 497
122–131 544
132–141 555
142–151 609
152–161 559
162–171 534
172–181 556
182–191 583
192–201 519
202–211 508
212–221 490
222–231 437
232–241 423
242–251 408 248
252–261 377
262–271 301
272–281 335
282–291 320
292–301 293
302–311 250
312–321 238
322–331 206
332–341 209
342–351 208
352–361 162
362–371 176
372–381 127
382–391 158
392–401 128
402–411 104
412–421 94
422–431 99
432–441 83
442–451 108
452–461 73
462–471 77
472–481 69
482–491 84
492–501 62
502–511 54
512–521 57
522–531 51
532–541 51
542–551 32
552–561 38
562–571 28
572–581 43
582–591 33
592–601 41
602–611 32
612–621 28
622–631 25
632–641 27
642–651 17
652–661 20
662–671 17
672–681 15
682–691 14
692–701 21
702–711 13
712–721 12
722–731 19
732–741 14
742–751 12
752–761 10
762–771 10
772–781 11
782–791 10
792–801 11
802–811 8
812–821 8
822–831 7
832–841 11
842–851 10
852–861 5
862–871 9
872–881 4
882–891 6
892–901 3
902–911 6
912–921 3
922–931 2
932–941 2
942–951 2
952–961 3
962–971 3
972–981 3
982–990 5
991+ 100
Download as CSV

Xiaobo Qu 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 Xiaobo Qu sits on this spectrum.

No. of scientists
200 400 600 800
Bar chart with 52 bars. Horizontal axis: D-Index, 30–31 to 131+. Vertical axis: number of scientists, 0 to 990. Most scientists, 990, have 36–37 D-Index. The last bar groups every scientist with 131 D-Index or more. The highlighted bar, 60–61 D-Index, is where this scientist sits. 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
30–31 D-Index 131+

This scientist: 61 D-Index — 79th percentile

79% of scientists in this discipline score the same or lower.

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

View D-Index distribution as a table
Number of Computer Science scientists by D-index, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
D-Index Scientists This scientist
30–31 879
32–33 983
34–35 918
36–37 990
38–39 968
40–41 907
42–43 821
44–45 763
46–47 689
48–49 543
50–51 543
52–53 518
54–55 500
56–57 458
58–59 400
60–61 337 61
62–63 308
64–65 292
66–67 249
68–69 213
70–71 192
72–73 189
74–75 165
76–77 139
78–79 119
80–81 121
82–83 113
84–85 88
86–87 87
88–89 75
90–91 69
92–93 57
94–95 46
96–97 38
98–99 34
100–101 36
102–103 27
104–105 37
106–107 18
108–109 31
110–111 19
112–113 16
114–115 12
116–117 20
118–119 15
120–121 5
122–123 20
124–125 8
126–127 5
128–129 7
130 3
131+ 98
Download as CSV

Research.com Recognitions

  • 2020 - Member of the European Academy of Sciences
  • 2020 - Member of Academia Europaea

Overview

Xiaobo Qu is affiliated with Xiamen University in China and has a considerable publication record focused on advanced techniques in medical imaging and related fields. Their research spans multiple areas in medicine and engineering, with a strong emphasis on medical imaging methodologies, particularly involving magnetic resonance imaging (MRI) and nuclear magnetic resonance (NMR) spectroscopy.

Their work encompasses the following main fields of study:

  • Medicine
  • Engineering

Within these broad disciplines, the following subfields are prominent in their research:

  • Radiology, Nuclear Medicine and Imaging
  • Nuclear and High Energy Physics
  • Biomedical Engineering
  • Computational Mechanics
  • Molecular Biology

Xiaobo Qu's research topics cover a range of advanced medical and imaging techniques:

  • Advanced MRI Techniques and Applications
  • Medical Imaging Techniques and Applications
  • NMR spectroscopy and applications
  • Sparse and Compressive Sensing Techniques
  • Advanced X-ray and CT Imaging
  • Advanced Neuroimaging Techniques and Applications
  • Metabolomics and Mass Spectrometry Studies

The scientist has contributed to several frequently publishing venues including:

  • arXiv (Cornell University)
  • 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
  • Journal of Magnetic Resonance
  • Medical Image Analysis
  • BMC Medical Imaging

Among their recent contributions, the following papers highlight their focus and collaborations:

  • Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy, 2020, Chemistry - A European Journal
  • A review on deep learning MRI reconstruction without fully sampled k-space, 2021, BMC Medical Imaging
  • Image reconstruction with low-rankness and self-consistency of k-space data in parallel MRI, 2020, Medical Image Analysis
  • Physics-Driven Synthetic Data Learning for Biomedical Magnetic Resonance: The imaging physics-based data synthesis paradigm for artificial intelligence, 2023, IEEE Signal Processing Magazine
  • A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation, 2024, Renewable Energy

Frequent collaborators include Di Guo, Zi Wang, Zhangren Tu, and Chen Qian, reflecting ongoing research partnerships across multiple projects.

The scientist has been recognized by membership in notable academic organizations:

  • Member of Academia Europaea (2020)
  • Member of the European Academy of Sciences (2020)

Best Publications

  • Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator

    Xiaobo Qu;Yingkun Hou;Fan Lam;Di Guo

  • Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment

    Weiming Lin;Tong Tong;Qinquan Gao;Di Guo

  • Bus stop-skipping scheme with random travel time

    Zhiyuan Liu;Yadan Yan;Yadan Yan;Xiaobo Qu;Yong Zhang

  • Ship collision risk assessment for the Singapore Strait

    Xiaobo Qu;Qiang Meng;Li Suyi

  • On the Impact of Cooperative Autonomous Vehicles in Improving Freeway Merging: A Modified Intelligent Driver Model-Based Approach

    Mofan Zhou;Xiaobo Qu;Sheng Jin

  • A recurrent neural network based microscopic car following model to predict traffic oscillation

    Mofan Zhou;Mofan Zhou;Xiaobo Qu;Xiaobo Qu;Xiaopeng Li

  • An overview of maritime waterway quantitative risk assessment models.

    Suyi Li;Qiang Meng;Xiaobo Qu

  • Undersampled MRI reconstruction with patch-based directional wavelets

    Xiaobo Qu;Di Guo;Bende Ning;Yingkun Hou

  • Development of an Efficient Driving Strategy for Connected and Automated Vehicles at Signalized Intersections: A Reinforcement Learning Approach

    Mofan Zhou;Yang Yu;Xiaobo Qu

  • Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach

    Xiaobo Qu;Yang Yu;Yang Yu;Mofan Zhou;Chin-Teng Lin

  • Fast Multiclass Dictionaries Learning With Geometrical Directions in MRI Reconstruction

    Zhifang Zhan;Jian-Feng Cai;Di Guo;Yunsong Liu

  • On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models

    Xiaobo Qu;Shuaian Wang;Jin Zhang

  • Estimation of rear-end vehicle crash frequencies in urban road tunnels

    Qiang Meng;Xiaobo Qu;Xiaobo Qu

  • Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging

    Yunsong Liu;Zhifang Zhan;Jian-Feng Cai;Di Guo

  • Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

    Xiaobo Qu;Yihui Huang;Hengfa Lu;Tianyu Qiu

  • Iterative thresholding compressed sensing MRI based on contourlet transform

    Xiaobo Qu;Weiru Zhang;Di Guo;Congbo Cai

  • Accelerated NMR Spectroscopy with Low‐Rank Reconstruction

    Xiaobo Qu;Maxim Mayzel;Jian Feng Cai;Zhong Chen

  • Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.

    Zongying Lai;Xiaobo Qu;Yunsong Liu;Di Guo

  • On the Stochastic Fundamental Diagram for Freeway Traffic: Model Development, Analytical Properties, Validation, and Extensive Applications

    Xiaobo Qu;Jin Zhang;Shuaian Wang

  • Vessel Collision Frequency Estimation in the Singapore Strait

    Jinxian Weng;Qiang Meng;Xiaobo Qu

  • Optimal electric bus fleet scheduling considering battery degradation and non-linear charging profile

    Le Zhang;Le Zhang;Shuaian Wang;Xiaobo Qu

  • A tree-structured crash surrogate measure for freeways

    Yan Kuang;Xiaobo Qu;Shuaian Wang

  • Bus dwell time estimation at bus bays: A probabilistic approach

    Qiang Meng;Xiaobo Qu

  • A probabilistic quantitative risk assessment model for the long-term work zone crashes

    Qiang Meng;Jinxian Weng;Xiaobo Qu

  • Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption

    Yiming Bie;Jinhua Ji;Xiangyu Wang;Xiangyu Wang;Xiaobo Qu

Frequent Co-Authors

Zhong Chen
Zhong Chen Nanyang Technological University
Shuaian Wang
Shuaian Wang Hong Kong Polytechnic University
Qiang Meng
Qiang Meng National University of Singapore
Jian-Feng Cai
Jian-Feng Cai Hong Kong University of Science and Technology
Zhiyuan Liu
Zhiyuan Liu Southeast University
Xiaopeng Li
Xiaopeng Li University of Wisconsin–Madison
Feng Huang
Feng Huang Sun Yat-sen University
Lu Zhen
Lu Zhen Shanghai University
Xi Peng
Xi Peng Sichuan University
Xiangyu Wang
Xiangyu Wang Curtin University

External Links

Wikipedia of Xiaobo Qu Personal website of Xiaobo Qu Google Scholar page

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