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Hiroyuki Uetani

Explore the profile of Hiroyuki Uetani including associated specialties, affiliations and a list of published articles. Areas
Snapshot
Articles 77
Citations 436
Followers 0
Related Specialties
Radiology
Neurology
Geriatrics
Top 10 Co-Authors
Toshinori Hirai
Takeshi Nakaura
Yasunori Nagayama
Masafumi Kidoh
Yasuyuki Yamashita
Mika Kitajima
Akitake Mukasa
Kosuke Morita
Minako Azuma
Seitaro Oda
Published In
J Comput Assist Tomogr
Neuroradiology
Magn Reson Med Sci
Jpn J Radiol
Eur Radiol
Affiliations
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Recent Articles
1.
Duplicated Cervical Internal Carotid Artery with Ipsilateral Persistent Stapedial Artery and Contralateral Aberrant Internal Carotid Artery: A Case Report
Hayashi H, Kiyosue H, Tamura Y, Yonemura M, Uchiumi S, Watanabe Y, et al.
J Neuroendovasc Ther . 2025 Feb; 19(1). PMID: 40007972
Objective: Duplication of extracranial cerebral arteries is a rare anatomical variant, most commonly observed in the vertebral artery. However, duplication of the cervical internal carotid artery (ICA) is extremely rare....
2.
Utilization of Heavily T2-Weighted Imaging and Dynamic Contrast-Enhanced 3T Magnetic Resonance Angiography for Diagnosing Spinal Extradural Arteriovenous Fistula: A Case Report
Murakami H, Mizutani H, Nakahara K, Uetani H, Kaku Y, Nakajima M, et al.
Cureus . 2025 Feb; 16(12):e76708. PMID: 39898149
Spinal arteriovenous fistula (SAVF), including spinal dural arteriovenous fistula and spinal extradural arteriovenous fistula (SEAVF), is a rare spinal vascular disorder characterized by abnormal intradural and extradural arteriovenous shunting, often...
3.
Deep Learning Reconstruction for Enhanced Resolution and Image Quality in Breath-Hold MRCP: A Preliminary Study
Shiraishi K, Nakaura T, Yoshida N, Matsuo K, Kobayashi N, Hokamura M, et al.
J Comput Assist Tomogr . 2025 Jan; PMID: 39761494
Objective: This preliminary study aims to assess the image quality of enhanced-resolution deep learning reconstruction (ER-DLR) in magnetic resonance cholangiopancreatography (MRCP) and compare it with non-ER-DLR MRCP images. Methods: Our...
4.
Usefulness of Dual-Energy CT for Differentiating Hemorrhage From Iodine Extravasation in Meningiomas After Preoperative Embolization
Uchimura R, Khant Z, Hayashi H, Nagayama Y, Uetani H, Kaku Y, et al.
J Comput Assist Tomogr . 2025 Jan; PMID: 39761489
Objective: Discriminating between hemorrhage and iodine extravasation can pose challenges in conventional computed tomography (CCT) images following preoperative embolization for meningioma. This study aimed to assess the efficacy of dual-energy...
5.
Dynamic susceptibility contrast perfusion MRI helps in differentiating double-expressor from non-double-expressor subtypes in primary central nervous system lymphoma
Sasaki G, Uetani H, Kuroda J, Kitajima M, Ishiuchi S, Sato K, et al.
Neuroradiology . 2024 Dec; PMID: 39699645
Purpose: In the 2016 WHO Classification of Lymphoid Tissue Neoplasms, co-expression of MYC and BCL2 is newly designated as double expressor lymphoma. Patients with primary central nervous system lymphoma with...
6.
Enhancing thin slice 3D T2-weighted prostate MRI with super-resolution deep learning reconstruction: Impact on image quality and PI-RADS assessment
Shiraishi K, Nakaura T, Kobayashi N, Uetani H, Nagayama Y, Kidoh M, et al.
Magn Reson Imaging . 2024 Dec; 117:110308. PMID: 39667642
Purposes: This study aimed to assess the effectiveness of Super-Resolution Deep Learning Reconstruction (SR-DLR) -a deep learning-based technique that enhances image resolution and quality during MRI reconstruction- in improving the...
7.
Machine Learning for Evaluating Vulnerable Plaque on Coronary Computed Tomography Using Spectral Imaging
Mochizuki J, Hata Y, Nakaura T, Hashimoto K, Uetani H, Nagayama Y, et al.
Circ Rep . 2024 Dec; 6(12):564-572. PMID: 39659635
Background: This study aimed to determine whether spectral imaging with dual-energy computed tomography (CT) can improve diagnostic performance for coronary plaque characterization. Methods And Results: We conducted a retrospective analysis...
8.
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations: a real-world clinical analysis
Kobayashi N, Nakaura T, Yoshida N, Nagayama Y, Kidoh M, Uetani H, et al.
Eur Radiol . 2024 Nov; PMID: 39613960
Purpose: The purpose of this study is to estimate the extent to which the implementation of deep learning reconstruction (DLR) may reduce the risk of radiation-induced cancer from CT examinations,...
9.
Performance of Multimodal Large Language Models in Japanese Diagnostic Radiology Board Examinations (2021-2023)
Nakaura T, Yoshida N, Kobayashi N, Nagayama Y, Uetani H, Kidoh M, et al.
Acad Radiol . 2024 Nov; PMID: 39521632
Rationale And Objectives: To evaluate the performance of various multimodal large language models (LLMs) in the Japanese Diagnostic Radiology Board Examinations (JDRBE) both with and without images. Materials And Methods:...
10.
Clinical Impact of Radiologist's Alert System on Patient Care for High-risk Incidental CT Findings: A Machine Learning-Based Risk Factor Analysis
Oda S, Chikamoto A, Khant Z, Uetani H, Kidoh M, Nagayama Y, et al.
Acad Radiol . 2024 Oct; 32(1):112-119. PMID: 39366804
Rationale And Objectives: Efficient communication between radiologists and clinicians ordering computed tomography (CT) examinations is crucial for managing high-risk incidental CT findings (ICTFs). Herein, we introduced a Radiologist's Alert and...