Fujita Shohei, Fushimi Yasutaka, Ito Rintaro, Matsui Yusuke, Tatsugami Fuminari, Fujioka Tomoyuki, Ueda Daiju, Fujima Noriyuki, Hirata Kenji, Tsuboyama Takahiro, Nozaki Taiki, Yanagawa Masahiro, Kamagata Koji, Kawamura Mariko, Yamada Akira, Nakaura Takeshi, Naganawa Shinji
Department of Radiology, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan.
Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan.
Jpn J Radiol. 2025 Mar;43(3):355-364. doi: 10.1007/s11604-024-01689-y. Epub 2024 Nov 16.
In this narrative review, we review the applications of artificial intelligence (AI) into clinical magnetic resonance imaging (MRI) exams, with a particular focus on Japan's contributions to this field. In the first part of the review, we introduce the various applications of AI in optimizing different aspects of the MRI process, including scan protocols, patient preparation, image acquisition, image reconstruction, and postprocessing techniques. Additionally, we examine AI's growing influence in clinical decision-making, particularly in areas such as segmentation, radiation therapy planning, and reporting assistance. By emphasizing studies conducted in Japan, we highlight the nation's contributions to the advancement of AI in MRI. In the latter part of the review, we highlight the characteristics that make Japan a unique environment for the development and implementation of AI in MRI examinations. Japan's healthcare landscape is distinguished by several key factors that collectively create a fertile ground for AI research and development. Notably, Japan boasts one of the highest densities of MRI scanners per capita globally, ensuring widespread access to the exam. Japan's national health insurance system plays a pivotal role by providing MRI scans to all citizens irrespective of socioeconomic status, which facilitates the collection of inclusive and unbiased imaging data across a diverse population. Japan's extensive health screening programs, coupled with collaborative research initiatives like the Japan Medical Imaging Database (J-MID), enable the aggregation and sharing of large, high-quality datasets. With its technological expertise and healthcare infrastructure, Japan is well-positioned to make meaningful contributions to the MRI-AI domain. The collaborative efforts of researchers, clinicians, and technology experts, including those in Japan, will continue to advance the future of AI in clinical MRI, potentially leading to improvements in patient care and healthcare efficiency.
在这篇叙述性综述中，我们回顾了人工智能（AI）在临床磁共振成像（MRI）检查中的应用，特别关注日本在该领域的贡献。在综述的第一部分，我们介绍了AI在优化MRI流程不同方面的各种应用，包括扫描协议、患者准备、图像采集、图像重建和后处理技术。此外，我们研究了AI在临床决策中日益增长的影响，特别是在分割、放射治疗计划和报告辅助等领域。通过强调在日本进行的研究，我们突出了该国对MRI中AI进步的贡献。在综述的后半部分，我们强调了使日本成为MRI检查中AI开发和实施独特环境的特征。日本的医疗保健格局具有几个关键因素，共同为AI研发创造了肥沃的土壤。值得注意的是，日本拥有全球人均MRI扫描仪密度最高的国家之一，确保了广泛的检查机会。日本的国民健康保险系统发挥着关键作用，为所有公民提供MRI扫描，而不论其社会经济地位如何，这有助于在不同人群中收集全面且无偏见的成像数据。日本广泛的健康筛查计划，加上诸如日本医学影像数据库（J-MID）等合作研究计划，使得能够汇总和共享大型高质量数据集。凭借其技术专长和医疗保健基础设施，日本在MRI-AI领域有能力做出有意义的贡献。包括日本在内的研究人员、临床医生和技术专家的共同努力，将继续推动临床MRI中AI的未来发展，有可能改善患者护理和医疗效率。
