提出新方法提升荧光分子断层成像精度与稳定性。
A Warm-basis Method for Bridging Learning and Iteration: a Case Study in Fluorescence Molecular Tomography
- 用学习引导的迭代投影法，融合神经网络与传统算法优势。
- 重建精度显著优于纯学习或纯迭代方法，且训练需求更低。
- 适合生物医学成像领域，尤其在数据受限时仍表现优异。
荧光分子断层成像（FMT）是生物医学研究中常用的无创光学成像技术，但深度重建常面临精度不足的问题，传统迭代方法即使采用先进正则化，仍存在较差的z轴分辨率。监督学习方法虽能提升恢复精度，但依赖大量高质量配对训练数据，实际难以获取。本文提出一种新颖的暖基迭代投影方法（WB-IPM），并建立其理论基础。该方法显著优于纯学习或纯迭代方法，且仅需基于真实值与神经网络输出方向差异的弱损失函数，大幅降低训练负担。误差分析及模拟与真实数据实验验证了其有效性。
原文摘要 · Abstract (English)
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 iterative-based 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.
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