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.
这项工作提出了GS-DOT，这是一种基于高斯泼溅（GS）的用于漫射光学层析成像（DOT）的新型图像重建框架。受渲染应用中GS的启发，吸收系数被表示为各向异性高斯原语的稀疏和，并通过解析梯度和Adam优化进行优化，以拟合测量得到的时间分辨点扩散函数。这是GS算法在光子扩散域中的首次应用，其中光线传输函数被扩散函数取代，从而能够对高散射介质中的光传输进行准确建模。在合成组织模型上的验证表明，对于干净和含噪信号，重建的吸收图在定位和量化方面都具有高准确性。GS-DOT展示了对噪声的高鲁棒性，并显示出对内存需求的巨大降低。