标题： $μ$NeuFMT：通过隐式神经表示的光学特性自适应荧光分子断层扫描 显示英文标题

标题： $μ$NeuFMT: Optical-Property-Adaptive Fluorescence Molecular Tomography via Implicit Neural Representation

摘要： 荧光分子断层成像（FMT）是一种用于非侵入性三维可视化荧光探针的有前途的技术，但由于其固有的不适定性和对不准确或通常未知的组织光学特性的依赖，其重建仍然具有挑战性。 虽然深度学习方法显示出前景，但其监督性质限制了其在训练数据之外的泛化能力。 为了解决这些问题，我们提出了$\mu$NeuFMT，这是一种自监督的FMT重建框架，它将基于隐式神经的场景表示与光子传播的显式物理建模相结合。 其关键创新在于在重建过程中同时优化荧光分布和光学特性（$\mu$），消除了对组织光学精确先验知识或预处理训练数据的需求。 我们证明，即使初始值严重错误（0.5$\times$到2$\times$的真实值），$\mu$NeuFMT也能稳健地恢复准确的荧光团分布和光学系数。 大量的数值、幻象和活体验证表明，$\mu$NeuFMT在各种异质场景中优于传统方法和监督深度学习方法。 我们的工作建立了一种新的鲁棒且准确的FMT重建范式，为复杂临床相关场景中的更可靠分子成像铺平了道路，例如荧光引导手术。 显示英文摘要

摘要： Fluorescence Molecular Tomography (FMT) is a promising technique for non-invasive 3D visualization of fluorescent probes, but its reconstruction remains challenging due to the inherent ill-posedness and reliance on inaccurate or often-unknown tissue optical properties. While deep learning methods have shown promise, their supervised nature limits generalization beyond training data. To address these problems, we propose $\mu$NeuFMT, a self-supervised FMT reconstruction framework that integrates implicit neural-based scene representation with explicit physical modeling of photon propagation. Its key innovation lies in jointly optimize both the fluorescence distribution and the optical properties ($\mu$) during reconstruction, eliminating the need for precise prior knowledge of tissue optics or pre-conditioned training data. We demonstrate that $\mu$NeuFMT robustly recovers accurate fluorophore distributions and optical coefficients even with severely erroneous initial values (0.5$\times$ to 2$\times$ of ground truth). Extensive numerical, phantom, and in vivo validations show that $\mu$NeuFMT outperforms conventional and supervised deep learning approaches across diverse heterogeneous scenarios. Our work establishes a new paradigm for robust and accurate FMT reconstruction, paving the way for more reliable molecular imaging in complex clinically related scenarios, such as fluorescence guided surgery.