Keigo Narita

708 total citations · 1 hit paper
15 papers, 521 citations indexed

About

Keigo Narita is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Surgery. According to data from OpenAlex, Keigo Narita has authored 15 papers receiving a total of 521 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Biomedical Engineering and 3 papers in Surgery. Recurrent topics in Keigo Narita's work include Advanced X-ray and CT Imaging (7 papers), Medical Imaging Techniques and Applications (6 papers) and Radiation Dose and Imaging (5 papers). Keigo Narita is often cited by papers focused on Advanced X-ray and CT Imaging (7 papers), Medical Imaging Techniques and Applications (6 papers) and Radiation Dose and Imaging (5 papers). Keigo Narita collaborates with scholars based in Japan, United States and Israel. Keigo Narita's co-authors include Yukiko Honda, Kazuo Awai, Yuko Nakamura, Toru Higaki, Motonori Akagi, Naruomi Akino, Jian Zhou, Yu Zhou, Fuminari Tatsugami and Shota Kondo and has published in prestigious journals such as Scientific Reports, European Radiology and Journal of Computer Assisted Tomography.

In The Last Decade

Keigo Narita

13 papers receiving 514 citations

Hit Papers

Deep learning reconstruct... 2019 2026 2021 2023 2019 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT
    2019 273 citations Motonori Akagi, Yuko Nakamura et al. European Radiology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Keigo Narita Japan 8 417 356 69 64 31 15 521
Motonori Akagi Japan 10 449 1.1× 342 1.0× 71 1.0× 71 1.1× 37 1.2× 13 563
Robert Peter Reimer Germany 14 254 0.6× 189 0.5× 41 0.6× 63 1.0× 28 0.9× 38 398
Nicola Fink Germany 16 508 1.2× 402 1.1× 74 1.1× 113 1.8× 12 0.4× 58 659
Taiping He China 14 319 0.8× 289 0.8× 62 0.9× 115 1.8× 26 0.8× 34 476
Felix G. Gassert Germany 12 234 0.6× 129 0.4× 59 0.9× 93 1.5× 34 1.1× 38 406
Adam Spandorfer United States 8 174 0.4× 179 0.5× 116 1.7× 58 0.9× 22 0.7× 10 336
Sylvain Bodard France 12 196 0.5× 163 0.5× 48 0.7× 156 2.4× 14 0.5× 55 403
Masahiro Hatemura Japan 16 928 2.2× 750 2.1× 59 0.9× 151 2.4× 24 0.8× 40 1.1k
Toshiharu Miyoshi Japan 20 756 1.8× 621 1.7× 51 0.7× 109 1.7× 105 3.4× 50 900
Jingyu Zhong China 13 300 0.7× 174 0.5× 45 0.7× 133 2.1× 48 1.5× 49 436

Countries citing papers authored by Keigo Narita

Since Specialization
Citations

This map shows the geographic impact of Keigo Narita's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Keigo Narita with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keigo Narita more than expected).

Fields of papers citing papers by Keigo Narita

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Keigo Narita. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Keigo Narita. The network helps show where Keigo Narita may publish in the future.

Co-authorship network of co-authors of Keigo Narita

This figure shows the co-authorship network connecting the top 25 collaborators of Keigo Narita. A scholar is included among the top collaborators of Keigo Narita based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Keigo Narita. Keigo Narita is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
1.
Honda, Yukiko, Keigo Narita, Yuko Nakamura, et al.. (2025). Effectiveness of a virtual reality-based interventional radiology simulator for medical student education. Japanese Journal of Radiology. 43(8). 1386–1392.
2.
Kondo, Shota, Yuko Nakamura, Takashi Nishihara, et al.. (2024). Utility of under-sampled scans with iterative reconstruction and high-frequency preserving transform for high spatial resolution magnetic resonance cholangiopancreatography. Japanese Journal of Radiology. 43(3). 463–471. 1 indexed citations
3.
Fukumoto, Wataru, et al.. (2024). Development of a deep-learning algorithm for age estimation on CT images of the vertebral column. Legal Medicine. 69. 102444–102444.
4.
Narita, Keigo, Yuko Nakamura, Toru Higaki, et al.. (2023). Iodine maps derived from sparse-view kV-switching dual-energy CT equipped with a deep learning reconstruction for diagnosis of hepatocellular carcinoma. Scientific Reports. 13(1). 3603–3603. 5 indexed citations
5.
Nakamura, Yuko, Shota Kondo, Keigo Narita, et al.. (2022). Understanding CT imaging findings based on the underlying pathophysiology in patients with small bowel ischemia. Japanese Journal of Radiology. 41(4). 353–366. 21 indexed citations
6.
Kondo, Shota, Yuko Nakamura, Toru Higaki, et al.. (2022). Utility of Wavelet Denoising with Geometry Factor Weighting for Gadoxetic Acid-enhanced Hepatobiliary-phase MR Imaging. Magnetic Resonance in Medical Sciences. 22(2). 241–252. 1 indexed citations
7.
Narita, Keigo, Yuko Nakamura, Toru Higaki, et al.. (2021). Utility of Radial Scanning for the Identification of Arterial Hypervascularity of Hepatocellular Carcinoma on Gadoxetic Acid–Enhanced Magnetic Resonance Images. Journal of Computer Assisted Tomography. 45(3). 359–366. 1 indexed citations
8.
Nakamura, Yuko, Toru Higaki, Yukiko Honda, et al.. (2021). Advanced CT techniques for assessing hepatocellular carcinoma. La radiologia medica. 126(7). 925–935. 52 indexed citations
9.
Nakamura, Yuko, Keigo Narita, Toru Higaki, et al.. (2021). Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT. European Radiology. 31(7). 4700–4709. 44 indexed citations
10.
Akagi, Motonori, Yuko Nakamura, Toru Higaki, et al.. (2020). Deep learning reconstruction of equilibrium phase CT images in obese patients. European Journal of Radiology. 133. 109349–109349. 20 indexed citations
11.
Narita, Keigo, Yuko Nakamura, Toru Higaki, et al.. (2020). Deep learning reconstruction of drip-infusion cholangiography acquired with ultra-high-resolution computed tomography. Abdominal Radiology. 45(9). 2698–2704. 30 indexed citations
12.
Honda, Yukiko, Yuko Nakamura, Jun Teishima, et al.. (2019). Clinical staging of upper urinary tract urothelial carcinoma for T staging: Review and pictorial essay. International Journal of Urology. 26(11). 1024–1032. 30 indexed citations
13.
Nakamura, Yuko, Toru Higaki, Fuminari Tatsugami, et al.. (2019). Possibility of Deep Learning in Medical Imaging Focusing Improvement of Computed Tomography Image Quality. Journal of Computer Assisted Tomography. 44(2). 161–167. 38 indexed citations
14.
Akagi, Motonori, Yuko Nakamura, Toru Higaki, et al.. (2019). Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. European Radiology. 29(11). 6163–6171. 273 indexed citations breakdown →
15.
Nakamura, Yuko, Toru Higaki, Takashi Nishihara, et al.. (2019). Pseudo-random Trajectory Scanning Suppresses Motion Artifacts on Gadoxetic Acid-enhanced Hepatobiliary-phase Magnetic Resonance Images. Magnetic Resonance in Medical Sciences. 19(1). 21–28. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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