Rintaro Ito

2.4k total citations · 3 hit papers
74 papers, 1.5k citations indexed

About

Rintaro Ito is a scholar working on Radiology, Nuclear Medicine and Imaging, Cellular and Molecular Neuroscience and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Rintaro Ito has authored 74 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Radiology, Nuclear Medicine and Imaging, 20 papers in Cellular and Molecular Neuroscience and 15 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Rintaro Ito's work include Radiomics and Machine Learning in Medical Imaging (26 papers), Cerebrospinal fluid and hydrocephalus (20 papers) and Medical Imaging Techniques and Applications (11 papers). Rintaro Ito is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (26 papers), Cerebrospinal fluid and hydrocephalus (20 papers) and Medical Imaging Techniques and Applications (11 papers). Rintaro Ito collaborates with scholars based in Japan, United States and Canada. Rintaro Ito's co-authors include Shinji Naganawa, Toshiaki Taoka, Hisashi Kawai, Rei Nakamichi, Toshiki Nakane, Shingo Iwano, Koji Kamagata, Daiju Ueda, Masahiro Yanagawa and Taiki Nozaki and has published in prestigious journals such as PLoS ONE, Scientific Reports and Radiology.

In The Last Decade

Rintaro Ito

65 papers receiving 1.5k citations

Hit Papers

align trajectories

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 →
Reproducibility of diffusion tensor image analysis along the perivascular space (DTI-ALPS) for evaluating interstitial fluid diffusivity and glymphatic function: CHanges in Alps index on Multiple conditiON acquIsition eXperiment (CHAMONIX) study

2021 179 citations Toshiaki Taoka, Rintaro Ito et al. Japanese Journal of Radiology profile →
Diffusion Tensor Image Analysis ALong the Perivascular Space (DTI-ALPS): Revisiting the Meaning and Significance of the Method

2024 141 citations Toshiaki Taoka, Rintaro Ito et al. Magnetic Resonance in Medical Sciences profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Rintaro Ito Japan	20	786	566	277	252	243	74	1.5k
Se Jin Cho South Korea	22	654 0.8×	284 0.5×	140 0.5×	56 0.2×	115 0.5×	54	1.7k
Joshua E. Medow United States	14	567 0.7×	215 0.4×	139 0.5×	48 0.2×	180 0.7×	35	1.2k
Hajime Yokota Japan	23	924 1.2×	249 0.4×	104 0.4×	30 0.1×	90 0.4×	166	2.2k
Miran Han South Korea	21	610 0.8×	112 0.2×	37 0.1×	98 0.4×	130 0.5×	58	1.2k
Kara M. Schwartz United States	20	340 0.4×	113 0.2×	54 0.2×	76 0.3×	57 0.2×	42	1.6k
Jared Pisapia United States	16	369 0.5×	136 0.2×	58 0.2×	27 0.1×	97 0.4×	31	882
Lola B. Chambless United States	25	241 0.3×	89 0.2×	217 0.8×	61 0.2×	57 0.2×	119	1.8k
Frank Gaillard Australia	19	548 0.7×	88 0.2×	33 0.1×	127 0.5×	44 0.2×	99	1.4k
Peter J. Kertes Canada	26	1.7k 2.2×	60 0.1×	121 0.4×	83 0.3×	82 0.3×	213	2.9k
Noriyuki Fujima Japan	25	1.1k 1.4×	38 0.1×	41 0.1×	258 1.0×	47 0.2×	116	1.8k

Countries citing papers authored by Rintaro Ito

Since Specialization

Citations

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

Fields of papers citing papers by Rintaro Ito

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rintaro Ito

This figure shows the co-authorship network connecting the top 25 collaborators of Rintaro Ito. A scholar is included among the top collaborators of Rintaro Ito 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 Rintaro Ito. Rintaro Ito 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

Naganawa, Shinji, Rintaro Ito, Mariko Kawamura, et al.. (2025). The Endolymph Signal in Non-contrast Enhanced 3D-real IR Image Differs between the Ears with and without Significant Endolymphatic Hydrops. Magnetic Resonance in Medical Sciences. 24(4). n/a–n/a. 2 indexed citations

Ito, Rintaro, Keita Kato, Hiroshi Ogawa, et al.. (2025). Assessing large language models for Lugano classification of malignant lymphoma in Japanese FDG-PET reports. PubMed. 9(1). 8–8. 1 indexed citations

Honda, Maya, Mami Iima, Masako Kataoka, et al.. (2025). Breast cancer imaging without gadolinium-based contrast agent: A review of current applications and future trends. Diagnostic and Interventional Imaging. 107(2). 51–61.

Minamimoto, Ryogo, et al.. (2025). Imaging insights of FDG-PET from neonates to infants. Japanese Journal of Radiology. 43(7). 1066–1077. 1 indexed citations

Kurokawa, Ryo, Akifumi Hagiwara, Rintaro Ito, et al.. (2025). Illuminating radiogenomic signatures in pediatric-type diffuse gliomas: insights into molecular, clinical, and imaging correlations. Part II: low-grade group. La radiologia medica. 130(9). 1503–1515.

Kurokawa, Ryo, Akifumi Hagiwara, Daiju Ueda, et al.. (2025). Illuminating radiogenomic signatures in pediatric-type diffuse gliomas: insights into molecular, clinical, and imaging correlations. Part I: high-grade group. La radiologia medica. 130(11). 1871–1887.

Naganawa, Shinji, Rintaro Ito, Mariko Kawamura, et al.. (2025). Ocular Solute Movement Direction and Intracranial Clearance via Vitreous Gadolinium-based Contrast Agent MR Imaging: Potential as a Novel Biomarker for Glymphatic Dysfunction. Magnetic Resonance in Medical Sciences. 25(1). n/a–n/a.

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

Abe, Shinji, Rintaro Ito, Ryogo Minamimoto, et al.. (2025). Predictive factors for the efficacy of radioiodine therapy in patients with Graves’ disease. Nuclear Medicine Communications. 46(12). 1180–1185.

10.

Nakamichi, Rei, Toshiaki Taoka, Rintaro Ito, et al.. (2024). Imaging evaluation and volumetric measurement of the space surrounding the diploic veins. Japanese Journal of Radiology. 42(9). 953–961. 1 indexed citations

11.

Ohashi, Toshio, et al.. (2024). Association between the Presence of the Parasagittal Cyst-like Structures and Cognitive Function. Magnetic Resonance in Medical Sciences. 24(4). n/a–n/a. 1 indexed citations

12.

Ueda, Daiju, Shannon L. Walston, Shohei Fujita, et al.. (2024). Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future. Diagnostic and Interventional Imaging. 105(11). 453–459. 27 indexed citations

13.

Matsui, Yusuke, Daiju Ueda, Shohei Fujita, et al.. (2024). Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature. Japanese Journal of Radiology. 43(2). 164–176. 7 indexed citations

14.

Naganawa, Shinji, Rintaro Ito, Mariko Kawamura, et al.. (2023). Association between the Putative Meningeal Lymphatics at the Posterior Wall of the Sigmoid Sinus and Delayed Contrast-agent Elimination from the Cerebrospinal Fluid. Magnetic Resonance in Medical Sciences. 23(1). 80–91. 10 indexed citations

15.

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

16.

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

17.

Fujima, Noriyuki, Koji Kamagata, Daiju Ueda, et al.. (2023). Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging. Magnetic Resonance in Medical Sciences. 22(4). 401–414. 14 indexed citations

18.

Hirata, Kenji, Koji Kamagata, Daiju Ueda, et al.. (2023). From FDG and beyond: the evolving potential of nuclear medicine. Annals of Nuclear Medicine. 37(11). 583–595. 15 indexed citations

19.

Taoka, Toshiaki, Rintaro Ito, Rei Nakamichi, et al.. (2023). Evaluation of alterations in interstitial fluid dynamics in cases of whole‐brain radiation using the diffusion‐weighted image analysis along the perivascular space method. NMR in Biomedicine. 37(7). e5030–e5030. 10 indexed citations

20.

Taoka, Toshiaki, Noriko Aida, Yuta Fujii, et al.. (2020). White matter microstructural changes in tuberous sclerosis: Evaluation by neurite orientation dispersion and density imaging (NODDI) and diffusion tensor images. Scientific Reports. 10(1). 17 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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