{"ID":2845407,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.04510","arxiv_id":"2511.04510","title":"$μ$NeuFMT: Optical-Property-Adaptive Fluorescence Molecular Tomography via Implicit Neural Representation","abstract":"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 $μ$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 ($μ$) during reconstruction, eliminating the need for precise prior knowledge of tissue optics or pre-conditioned training data. We demonstrate that $μ$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 $μ$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.","short_abstract":"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, th...","url_abs":"https://arxiv.org/abs/2511.04510","url_pdf":"https://arxiv.org/pdf/2511.04510v1","authors":"[\"Shihan Zhao\",\"Jianru Zhang\",\"Yanan Wu\",\"Linlin Li\",\"Siyuan Shen\",\"Xingjun Zhu\",\"Guoyan Zheng\",\"Jiahua Jiang\",\"Wuwei Ren\"]","published":"2025-11-06T16:28:30Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\",\"physics.optics\"]","methods":"[]","has_code":false}