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Magnetic Particle Imaging (MPI) has emerged as a groundbreaking cellular tracking technology with unique advantages in Stem-cell and T-cell therapy monitoring. However, cultivating multidisciplinary talent for MPI applications remains particularly challenging due to the technology’s inherent integration of materials science, electromagnetics, and image processing disciplines. Our study systematically compiled over 1000 research articles, 50 graduate theses, 100 e-books, and 200 course materials to establish an MPI-specific knowledge base using Tencent IMA and Metaso AI platforms. Quantitative assessment through four evaluation categories (30 quiz questions, 5 examination problems, 4 report templates, and 4 programming tasks) demonstrated that Metaso AI exhibited superior temporal stability with an average response time of 1.76 min. Compared to Tencent IMA, it showed improvements of 15.2% in accuracy, 18.7% in professionalism, 12.4% in organizational clarity, and a 10–30% lower standard deviation across all metrics. The analysis revealed that Metaso AI excels in delivering comprehensive multidimensional analysis, whereas Tencent IMA focuses on concise conclusion delivery, making it suitable for time-sensitive educational applications. This complementary dual-platform approach provides a scalable solution for MPI education, effectively enhancing teaching efficiency and quality while addressing the critical need for interdisciplinary training in this emerging field.

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Research on Construction Methods of Educational Agents for Magnetic Particle Imaging

  • 会议论文

  • 发表日期：

    2026年

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  • Yiwen Li,
  • Chenxing Hu,
  • Yongchen Gou,
  • Zhongwei Bian,
  • Weihua Li,
  • Jia Luo,
  • Mingli Peng,
  • Hui Hui,
  • Jie Tian,
  • Tanping Li,
  • Qiguang Miao,
  • Guang Jia

摘要

Magnetic Particle Imaging (MPI) has emerged as a groundbreaking cellular tracking technology with unique advantages in Stem-cell and T-cell therapy monitoring. However, cultivating multidisciplinary talent for MPI applications remains particularly challenging due to the technology’s inherent integration of materials science, electromagnetics, and image processing disciplines. Our study systematically compiled over 1000 research articles, 50 graduate theses, 100 e-books, and 200 course materials to establish an MPI-specific knowledge base using Tencent IMA and Metaso AI platforms. Quantitative assessment through four evaluation categories (30 quiz questions, 5 examination problems, 4 report templates, and 4 programming tasks) demonstrated that Metaso AI exhibited superior temporal stability with an average response time of 1.76 min. Compared to Tencent IMA, it showed improvements of 15.2% in accuracy, 18.7% in professionalism, 12.4% in organizational clarity, and a 10–30% lower standard deviation across all metrics. The analysis revealed that Metaso AI excels in delivering comprehensive multidimensional analysis, whereas Tencent IMA focuses on concise conclusion delivery, making it suitable for time-sensitive educational applications. This complementary dual-platform approach provides a scalable solution for MPI education, effectively enhancing teaching efficiency and quality while addressing the critical need for interdisciplinary training in this emerging field.