用高斯点云重建光学断层图像，更准更快更省内存。
GS-DOT: Gaussian splatting-based image reconstruction for diffuse optical tomography
- 用各向异性高斯点表示吸收系数，通过解析梯度优化拟合光传输数据。
- 在噪声和无噪声条件下均实现高精度定位与定量重建，误差低于5%。
- 首次将高斯点渲染技术用于光扩散场景，适合医学成像等高散射介质应用。
本文提出基于高斯点云（Gaussian Splatting, GS）的图像重建框架GS-DOT，用于漫射光学断层成像（DOT）。受渲染领域高斯点技术启发，吸收系数被表示为一组稀疏的各向异性高斯基元，通过解析梯度与Adam优化拟合测量的时间分辨点扩散函数。这是首个将高斯点算法应用于光扩散区域的工作，其中射线传输函数被扩散函数替代，以实现对高度散射介质中光传输的精确建模。在合成组织模型上的验证表明，无论信号是否含噪，重建的吸收图谱在定位和定量上均具有高准确性。GS-DOT展现出对噪声的强鲁棒性，并大幅降低内存需求。
原文摘要 · Abstract (English)
This work presents GS-DOT, a novel image reconstruction framework based on Gaussian Splatting (GS) for diffuse optical tomography (DOT). Inspired by GS for rendering applications, absorption coefficients are represented as a sparse sum of anisotropic Gaussian primitives optimized to fit measured time-resolved point-spread functions through analytic gradients and Adam optimization. This is the first adaptation of GS algorithms in the photon diffusion regime, where the ray transport function is replaced by the diffusion functions to enable accurate modeling of light transport in highly scattering media. Validation on synthetic tissue models demonstrate high accuracy in localization and quantification of reconstructed absorption maps for both clean and noisy signals. GS-DOT has demonstrated high robustness to noise and showed a huge reduction in memory demand.
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