{"type":"ScholarlyArticle","title":"STAR-Net: Physics Inspired Spectral Topology Aware Reconstruction Network for Single-View Fluorescence Molecular Tomography","authors":[{"name":"Xiangzheng Li","slug":"xiangzheng-li","given":"Xiangzheng","family":"Li","affiliation":""},{"name":"Jian Zhang","slug":"jian-zhang","given":"Jian","family":"Zhang","affiliation":""},{"name":"Mengxiang Chu","slug":"mengxiang-chu","given":"Mengxiang","family":"Chu","affiliation":""},{"name":"Xiaoli Luo","slug":"xiaoli-luo","given":"Xiaoli","family":"Luo","affiliation":""},{"name":"Hongbo Guo","slug":"hongbo-guo","given":"Hongbo","family":"Guo","affiliation":""},{"name":"Xiaowei He","slug":"xiaowei-he","given":"Xiaowei","family":"He","affiliation":""}],"abstract":"Fluorescence molecular tomography (FMT) serves as a pivotal modality for preclinical tumor screening. While single-view FMT offers distinct advantages in data acquisition efficiency and cost-effectiveness, the scarcity of projection views severely exacerbates photon scattering-induced depth ambiguity, rendering 3D volumetric recovery a highly ill-posed inverse problem. To address these challenges, we propose a physics-inspired spectral topology aware reconstruction network (STAR-Net). Specifically, STAR-Net establishes a synergistic framework: initially, a frequency domain decoupling strategy is introduced to simulate the physical characteristics of diffuse light fields; building on this, a differentiable inverse spectral gating (DISG) mechanism is utilized to explicitly impose low-pass spectral regularization for precise depth recovery; and further, a dual-domain synergistic module is integrated to dynamically fuse spatial and frequency features, achieving high-fidelity detail preservation. Extensive experiments on the Digimouse benchmark demonstrate that the proposed STAR-Net achieves the highest dice coefficient under single-view conditions, validating that explicit spectral topology modeling is a powerful paradigm for mitigating depth ambiguity.","doi":"10.24963/ijcai.2026/754","url":"https://ijcai.org/proceedings/2026/754","pdf":"https://ijcai.org/proceedings/2026/0754.pdf","markdown":"https://ijcai.org/proceedings/2026/754.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/754","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Health","track":"AI and Health","number":754,"pages":{"first":6779,"last":6787},"keywords":["Explainable AI: Explainable AI","Medical imaging: Medical imaging"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}