SV-FMT-DualDiff: Dual-Path Conditional Diffusion for Single-View Tomography

Fluorescence Molecular Tomography (FMT) is an essential imaging technique for in vivo 3D biomolecular visualization. However, its application for rapid tomography generally remains unexplored since rapid imaging will inevitably require sparse projections, which makes the inverse problem extremely ill-posed and consequently causes traditional methods to fail. To overcome this significant challenge, this paper presents Single-View Fluorescence Molecular Tomography based on Dual-Path conditional Diffusion (SV-FMT-DualDiff), a novel paradigm that integrates the conditional diffusion denoising prior with physics-consistency for high-fidelity and rapid single-view reconstructions. For effective conditional control, the Tomographic Lifting Representation (TLR) enhances 3D volumetric conditioning information extracted from 2D projection features. Besides, a dual-path conditioning mechanism synergistically combines Local Guidance with Multi-feature Fusion (LGMF) and Global Guidance with Discrepancy-Common transformer (GGDC) to guide the denoising process. LGMF enhances local fine-grained detail and temporal awareness, while GGDC aligns noise with conditioning features before interaction to reinforce global correlations. Extensive numerical and in vivo experiments demonstrate that our method outperforms state-of-the-art techniques, delivering exceptional spatial resolution, robustness, and generalization capability. In dual-target reconstruction with a 0.5 mm edge-to-edge distance across different target locations, our method achieves CNR of 25.03 ± 0.74, Dice of 0.88 ± 0.01, LE of 0.026 ± 0.005 cm, and NMSE of 0.23 ± 0.01. This work offers a reliable solution for rapid 3D fluorescent imaging in biomedical research, such as tumor studies and drug development. Furthermore, it shows strong potential for extension to other imaging modalities by addressing severely ill-posed sparse-angle tomography for rapid, high-quality reconstruction.