Ayami Ohno Kishimoto

411 total citations
17 papers, 280 citations indexed

About

Ayami Ohno Kishimoto is a scholar working on Radiology, Nuclear Medicine and Imaging, Pathology and Forensic Medicine and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Ayami Ohno Kishimoto has authored 17 papers receiving a total of 280 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Radiology, Nuclear Medicine and Imaging, 3 papers in Pathology and Forensic Medicine and 3 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Ayami Ohno Kishimoto's work include MRI in cancer diagnosis (11 papers), Advanced Neuroimaging Techniques and Applications (7 papers) and Radiomics and Machine Learning in Medical Imaging (5 papers). Ayami Ohno Kishimoto is often cited by papers focused on MRI in cancer diagnosis (11 papers), Advanced Neuroimaging Techniques and Applications (7 papers) and Radiomics and Machine Learning in Medical Imaging (5 papers). Ayami Ohno Kishimoto collaborates with scholars based in Japan, Germany and United States. Ayami Ohno Kishimoto's co-authors include Mami Iima, Masako Kataoka, Maya Honda, Kaori Togashi, Eric E. Sigmund, Akane Ohashi, Masakazu Toi, Kanae K. Miyake, Nathan V. Welham and Yo Kishimoto and has published in prestigious journals such as Journal of Magnetic Resonance Imaging, Laboratory Investigation and Journal of Anatomy.

In The Last Decade

Ayami Ohno Kishimoto

17 papers receiving 279 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ayami Ohno Kishimoto Japan 9 232 31 27 24 20 17 280
Duraisamy Ravichandran United Kingdom 4 202 0.9× 30 1.0× 11 0.4× 9 0.4× 8 0.4× 10 251
Iwan Harries United Kingdom 9 132 0.6× 51 1.6× 6 0.2× 24 1.0× 10 0.5× 24 269
J.A. Messer United States 6 64 0.3× 7 0.2× 27 1.0× 67 2.8× 11 0.6× 17 164
Maya Honda Japan 10 376 1.6× 51 1.6× 3 0.1× 15 0.6× 25 1.3× 43 407
H. Oellinger Germany 8 258 1.1× 125 4.0× 15 0.6× 74 3.1× 19 0.9× 18 362
Averi E. Kitsch United States 8 393 1.7× 62 2.0× 3 0.1× 20 0.8× 33 1.6× 9 431
Laura Kerschke Germany 9 57 0.2× 13 0.4× 5 0.2× 59 2.5× 37 1.9× 20 162
Piotr Kędzierawski Poland 7 46 0.2× 7 0.2× 6 0.2× 21 0.9× 21 1.1× 34 129
Mario Mileto Italy 6 40 0.2× 22 0.7× 6 0.2× 15 0.6× 8 0.4× 11 108
Y. Rolland France 6 70 0.3× 7 0.2× 5 0.2× 35 1.5× 9 0.5× 15 175

Countries citing papers authored by Ayami Ohno Kishimoto

Since Specialization
Citations

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

Fields of papers citing papers by Ayami Ohno Kishimoto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ayami Ohno Kishimoto. 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 Ayami Ohno Kishimoto. The network helps show where Ayami Ohno Kishimoto may publish in the future.

Co-authorship network of co-authors of Ayami Ohno Kishimoto

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

All Works

17 of 17 papers shown
1.
Kataoka, Masako, Mami Iima, Maya Honda, et al.. (2023). Evaluation of breast lesions based on modified BI-RADS using high-resolution readout-segmented diffusion-weighted echo-planar imaging and T2/T1-weighted image. Magnetic Resonance Imaging. 98. 132–139. 4 indexed citations
2.
Honda, Maya, Masako Kataoka, Mami Iima, et al.. (2022). Visual Evaluation of Ultrafast MRI in the Assessment of Residual Breast Cancer After Neoadjuvant Systemic Therapy: A Preliminary Study Association with Subtype. Tomography. 8(3). 1522–1533. 7 indexed citations
3.
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5.
Yamamoto, Hirotaka, Mami Iima, Yo Kishimoto, et al.. (2022). Preoperative Localization of Parathyroid Adenomas with Diffusion MR Imaging: Readout-segmented versus Single-shot Echo-planar Imaging. Magnetic Resonance in Medical Sciences. 22(1). 79–85. 1 indexed citations
6.
Kawai, Yoshitaka, Mami Iima, Hirotaka Yamamoto, et al.. (2021). The added value of non-contrast 3-Tesla MRI for the pre-operative localization of hyperparathyroidism. Brazilian Journal of Otorhinolaryngology. 88. S58–S64. 3 indexed citations
7.
Kishimoto, Ayami Ohno, Yo Kishimoto, Xudong Shi, et al.. (2021). High‐resolution magnetic resonance and mass spectrometry imaging of the human larynx. Journal of Anatomy. 239(3). 545–556. 4 indexed citations
8.
Iima, Mami, Masako Kataoka, Maya Honda, et al.. (2021). The Rate of Apparent Diffusion Coefficient Change With Diffusion Time on Breast Diffusion-Weighted Imaging Depends on Breast Tumor Types and Molecular Prognostic Biomarker Expression. Investigative Radiology. 56(8). 501–508. 21 indexed citations
9.
Kishimoto, Ayami Ohno, Masako Kataoka, Mami Iima, et al.. (2020). The comparison of high-resolution diffusion weighted imaging (DWI) with high-resolution contrast-enhanced MRI in the evaluation of breast cancers. Magnetic Resonance Imaging. 71. 161–169. 16 indexed citations
10.
Honda, Maya, Masako Kataoka, Mami Iima, et al.. (2020). Background parenchymal enhancement and its effect on lesion detectability in ultrafast dynamic contrast-enhanced MRI. European Journal of Radiology. 129. 108984–108984. 19 indexed citations
11.
Honda, Maya, Masako Kataoka, Kosuke Kawaguchi, et al.. (2020). Subcategory classifications of Breast Imaging and Data System (BI-RADS) category 4 lesions on MRI. Japanese Journal of Radiology. 39(1). 56–65. 15 indexed citations
12.
Ohashi, Akane, Masako Kataoka, Mami Iima, et al.. (2020). A multiparametric approach to diagnosing breast lesions using diffusion-weighted imaging and ultrafast dynamic contrast-enhanced MRI. Magnetic Resonance Imaging. 71. 154–160. 14 indexed citations
13.
Kishimoto, Ayami Ohno, Masako Kataoka, Mami Iima, et al.. (2020). Evaluation of Malignant Breast Lesions Using High-resolution Readout-segmented Diffusion-weighted Echo-planar Imaging: Comparison with Pathology. Magnetic Resonance in Medical Sciences. 20(2). 204–215. 18 indexed citations
14.
Kishimoto, Yo, Ayami Ohno Kishimoto, Yosuke Yamada, et al.. (2019). Dedifferentiated liposarcoma of the thyroid gland: A case report. Molecular and Clinical Oncology. 11(3). 219–224. 4 indexed citations
15.
Iima, Mami, Maya Honda, Eric E. Sigmund, et al.. (2019). Diffusion MRI of the breast: Current status and future directions. Journal of Magnetic Resonance Imaging. 52(1). 70–90. 118 indexed citations
16.
Kishimoto, Yo, et al.. (2016). Modeling fibrosis using fibroblasts isolated from scarred rat vocal folds. Laboratory Investigation. 96(7). 807–816. 21 indexed citations
17.
Kishimoto, Ayami Ohno, Yo Kishimoto, David L. Young, et al.. (2016). High- and ultrahigh-field magnetic resonance imaging of naïve, injured and scarred vocal fold mucosae in rats. Disease Models & Mechanisms. 9(11). 1397–1403. 12 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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2026