FDMamba-FMT: Physical Consistency-Guided Feature-Decoupled Mamba for Fluorescence Molecular Tomography

—Fluorescence molecular tomography (FMT) enables noninvasive 3D imaging of fluorescent probes, facilitating early tumor detection and drug metabolism studies. However, in highly scattering biological tissues, it is constrained by limited imaging depth and suboptimal spatial resolution. Although existing deep learning methods have enhanced reconstruction quality, they frequently induce physical modeling biases by overlooking the radial aggregation characteristics of fluorescent probes, and introduce reconstruction errors due to the inadequate characterization of photon transport dynamics in tissues. These issues ultimately manifest as target shape distortions, edge blurring, and noise sensitivity. To address these issues, we propose FDMamba-FMT, a feature-decoupled state-space model-based framework for FMT reconstruction. We design a physics-prior-guided spiral-centered scanning path that explicitly captures the radial gradient distribution of fluorescence intensity, effectively mitigating the physical biases arising from overlooked radiative attenuation in modeling. Furthermore, we introduce a hierarchical feature decoupling mechanism, utilizing pixel-level and region-level Mamba modules to suppress local noise and enhance representations in globally attenuated regions, respectively, thereby substantially mitigating reconstruction biases arising from signal attenuation-noise coupling. Experimental results demonstrate that, across simulation, phantom, and in vivo scenarios, the proposed method achieves strong overall reconstruction performance compared with state-of-the-art approaches, yielding reconstructions that are more physically consistent, structurally intact, and noise-reduced. This work holds significant promise for advancing precise in vivo tumor localization and 3D dynamic imaging.