j9九游会

报告问题 (Title)：A Warm-basis Method for Bridging Learning and Iteration: a Case Study in Fluorescence Molecular Tomography（衔接学习与迭代的暖基要领——以荧光分子断层成像为例）

报告人 (Speaker)：姜嘉骅 副教授（上？
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报告时间 (Time)：2026年4月25日（周六）10:00-10:30

报告所在 (Place)：校本部GJ303

约请人(Inviter)：纪丽洁

主理部分：理学院数学系

摘要：Fluorescence Molecular Tomography (FMT) is a widely used non-invasive optical imaging technology in biomedical research. It usually faces significant accuracy challenges in depth reconstruction, and conventional iterative methods struggle with poor z-resolution even with advanced regularization. Supervised learning approaches can improve recovery accuracy but rely on large, high-quality paired training dataset that is often impractical to acquire in practice. This naturally raises the question of how learning-based approaches can be effectively combined with iterative schemes to yield more accurate and stable algorithms. In this work, we present a novel warm-basis iterative projection method (WB-IPM) and establish its theoretical underpinnings. The method is able to achieve significantly more accurate reconstructions than the learning-based and iterativebased methods. In addition, it allows a weaker loss function depending solely on the directional component of the difference between ground truth and neural network output, thereby substantially reducing the training effort. These features are justified by our error analysis as well as simulated and real-data experiments.