Takeshi Nakaura

8.3k total citations · 1 hit paper
309 papers, 5.9k citations indexed

About

Takeshi Nakaura is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Takeshi Nakaura has authored 309 papers receiving a total of 5.9k indexed citations (citations by other indexed papers that have themselves been cited), including 252 papers in Radiology, Nuclear Medicine and Imaging, 154 papers in Biomedical Engineering and 59 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Takeshi Nakaura's work include Advanced X-ray and CT Imaging (150 papers), Radiation Dose and Imaging (131 papers) and Cardiac Imaging and Diagnostics (82 papers). Takeshi Nakaura is often cited by papers focused on Advanced X-ray and CT Imaging (150 papers), Radiation Dose and Imaging (131 papers) and Cardiac Imaging and Diagnostics (82 papers). Takeshi Nakaura collaborates with scholars based in Japan, United States and Finland. Takeshi Nakaura's co-authors include Yasuyuki Yamashita, Kazuo Awai, Yoshinori Funama, Seitaro Oda, Daisuke Utsunomiya, Masafumi Kidoh, Yasunori Nagayama, Toshinori Hirai, Tomohiro Namimoto and Yumi Yanaga and has published in prestigious journals such as Circulation, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Takeshi Nakaura

287 papers receiving 5.8k citations

Hit Papers

align trajectories sort by overperformance

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.

Fairness of artificial intelligence in healthcare: review and recommendations

2023 236 citations Daiju Ueda, Shohei Fujita et al. Japanese Journal of Radiology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Takeshi Nakaura Japan	39	4.3k	2.8k	1.1k	645	417	309	5.9k
Fuminari Tatsugami Japan	30	2.7k 0.6×	1.4k 0.5×	519 0.5×	615 1.0×	296 0.7×	114	3.6k
Renato Cuocolo Italy	36	2.6k 0.6×	818 0.3×	1.1k 1.0×	485 0.8×	192 0.5×	183	3.9k
Carlo N. De Cecco United States	48	5.6k 1.3×	3.8k 1.4×	909 0.9×	1.6k 2.5×	1.5k 3.5×	270	7.3k
Bettina Baeßler Germany	30	3.0k 0.7×	1.0k 0.4×	735 0.7×	478 0.7×	830 2.0×	100	4.3k
Changhong Liang China	41	5.5k 1.3×	1.1k 0.4×	2.0k 1.9×	983 1.5×	193 0.5×	191	7.3k
Kazuo Awai Japan	50	6.2k 1.4×	3.6k 1.3×	2.0k 1.9×	1.5k 2.3×	731 1.8×	433	9.1k
Janita E. van Timmeren Netherlands	22	5.5k 1.3×	1.6k 0.6×	2.2k 2.1×	680 1.1×	64 0.2×	49	6.3k
Lincoln L. Berland United States	39	3.3k 0.8×	1.1k 0.4×	1.6k 1.5×	2.5k 3.9×	318 0.8×	120	7.3k
Wouter van Elmpt Netherlands	44	8.4k 1.9×	2.5k 0.9×	4.2k 4.0×	801 1.2×	102 0.2×	193	10.2k
Daniel T. Boll United States	37	3.8k 0.9×	2.4k 0.9×	973 0.9×	748 1.2×	218 0.5×	167	5.3k

Countries citing papers authored by Takeshi Nakaura

Since Specialization

Citations

This map shows the geographic impact of Takeshi Nakaura'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 Takeshi Nakaura with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takeshi Nakaura more than expected).

Fields of papers citing papers by Takeshi Nakaura

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Takeshi Nakaura. 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 Takeshi Nakaura. The network helps show where Takeshi Nakaura may publish in the future.

Co-authorship network of co-authors of Takeshi Nakaura

This figure shows the co-authorship network connecting the top 25 collaborators of Takeshi Nakaura. A scholar is included among the top collaborators of Takeshi Nakaura 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 Takeshi Nakaura. Takeshi Nakaura is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Nagayama, Yasunori, T Inoue, Yoshinori Funama, et al.. (2025). Super-resolution deep-learning reconstruction with 1024 matrix improves CT image quality for pancreatic ductal adenocarcinoma assessment. European Journal of Radiology. 184. 111953–111953. 2 indexed citations

Nozaki, Taiki, Masahiro Hashimoto, Daiju Ueda, et al.. (2025). Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?. La radiologia medica. 130(5). 587–597. 1 indexed citations

Nakaura, Takeshi, Naofumi Yoshida, Hiroyuki Uetani, et al.. (2025). Impact of super-resolution deep learning-based reconstruction for hippocampal MRI: A volunteer and phantom study. European Journal of Radiology. 191. 112289–112289.

Kidoh, Masafumi, Seitaro Oda, Seiji Takashio, et al.. (2025). MRI-Extracellular Volume Fraction Versus Histological Amyloid Load in Cardiac Amyloidosis: The Importance of T2 Mapping. Circulation Cardiovascular Imaging. 18(5). e017427–e017427. 2 indexed citations

Funama, Yoshinori, Yasunori Nagayama, Daisuke Sakabe, et al.. (2024). Advances in spatial resolution and radiation dose reduction using super-resolution deep learning–based reconstruction for abdominal computed tomography: A phantom study. Academic Radiology. 32(3). 1517–1524. 2 indexed citations

Nakaura, Takeshi, Rintaro Ito, Daiju Ueda, et al.. (2024). The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI. Japanese Journal of Radiology. 42(7). 685–696. 53 indexed citations

Hata, Yoshiki, Takeshi Nakaura, Katsushi Hashimoto, et al.. (2024). Machine Learning for Evaluating Vulnerable Plaque on Coronary Computed Tomography Using Spectral Imaging. Circulation Reports. 6(12). 564–572.

Masuda, Takanori, Yoshinori Funama, Takeshi Nakaura, et al.. (2023). Utilidad de la línea orbitomeatal superior sin incluir los cristalinos en la exploración con diferentes voltajes del tubo en la TC craneal pediátrica. Radiología. 66(5). 403–409.

Nagayama, Yasunori, Hiroyuki Uetani, Makoto Goto, et al.. (2023). Deep learning-based reconstruction can improve the image quality of low radiation dose head CT. European Radiology. 33(5). 3253–3265. 19 indexed citations

10.

Tatsugami, Fuminari, Takeshi Nakaura, Masahiro Yanagawa, et al.. (2023). Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction. Diagnostic and Interventional Imaging. 104(11). 521–528. 28 indexed citations

11.

Masuda, Takanori, Takeshi Nakaura, Yoshinori Funama, et al.. (2023). RADIATION DOSE REDUCTION AT LOW TUBE VOLTAGE WITH CORONARY ARTERY BYPASS GRAFT COMPUTED TOMOGRAPHY ANGIOGRAPHY BASED ON THE CONTRAST NOISE RATIO INDEX. Radiation Protection Dosimetry. 199(6). 527–532.

12.

Yamada, Akira, Koji Kamagata, Kenji Hirata, et al.. (2023). Clinical applications of artificial intelligence in liver imaging. La radiologia medica. 128(6). 655–667. 23 indexed citations

13.

Kidoh, Masafumi, Seitaro Oda, Seiji Takashio, et al.. (2022). CT Extracellular Volume Fraction versus Myocardium-to-Lumen Signal Ratio for Cardiac Amyloidosis. Radiology. 306(3). e220542–e220542. 18 indexed citations

14.

Nagayama, Yasunori, Daisuke Sakabe, Makoto Goto, et al.. (2021). Deep Learning–based Reconstruction for Lower-Dose Pediatric CT: Technical Principles, Image Characteristics, and Clinical Implementations. Radiographics. 41(7). 1936–1953. 51 indexed citations

15.

Yoshida, Morikatsu, Daisuke Utsunomiya, T Inoue, et al.. (2019). Prevalence of extracardiac findings in patients undergoing coronary computed tomography and additional low-dose whole-body computed tomography. Japanese Journal of Radiology. 38(2). 144–153. 2 indexed citations

16.

Nagayama, Yasunori, Seitaro Oda, Takeshi Nakaura, et al.. (2018). Radiation Dose Reduction at Pediatric CT: Use of Low Tube Voltage and Iterative Reconstruction. Radiographics. 38(5). 1421–1440. 100 indexed citations

17.

Sueta, Daisuke, Daisuke Utsunomiya, Yasuhiro Izumiya, et al.. (2018). Novel assessment of retrospective on-demand analysis of venous thromboembolism by dual-layer spectral-detector CT. Journal of Cardiology Cases. 18(3). 88–91. 2 indexed citations

18.

Yoshida, Morikatsu, Takeshi Nakaura, T Inoue, et al.. (2018). Magnetic resonance cholangiopancreatography with GRASE sequence at 3.0T: does it improve image quality and acquisition time as compared with 3D TSE?. European Radiology. 28(6). 2436–2443. 43 indexed citations

19.

Nakaura, Takeshi, Yuji Iyama, Masafumi Kidoh, et al.. (2018). Spiral flow-generating tube for saline chaser improves aortic enhancement in Gd-EOB-DTPA-enhanced hepatic MRI. European Radiology. 29(4). 2009–2016. 2 indexed citations

20.

Inoue, T, Takeshi Nakaura, Morikatsu Yoshida, et al.. (2017). Diagnosis of small posterior fossa stroke on brain CT: effect of iterative reconstruction designed for brain CT on detection performance. European Radiology. 27(9). 3710–3715. 11 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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