Light scattering imposes a major obstacle for imaging objects seated deeply in turbid media, such as biological tissues and foggy air. Diffuse optical tomography (DOT) tackles scattering by volumetrically recovering the optical absorbance and has shown significance in medical imaging, remote sensing and autonomous driving. A conventional DOT reconstruction paradigm necessitates discretizing the object volume into voxels at a pre-determined resolution for modelling diffuse light propagation and the resulting spatial resolution of the reconstruction is generally limited. We propose NeuDOT, a novel DOT scheme based on neural fields (NF) to continuously encode the optical absorbance within the volume and subsequently bridge the gap between model accuracy and high resolution. Comprehensive experiments demonstrate that NeuDOT achieves submillimetre lateral resolution and resolves complex 3D objects at 14 mm-depth, outperforming the state-of-the-art methods. NeuDOT is a non-invasive, high-resolution and computationally efficient tomographic method, and unlocks further applications of NF involving light scattering.

翻译：光散射是对位于浑浊介质（如生物组织和雾状空气）深处的物体进行成像的主要障碍。漫射光学断层扫描（DOT）通过体积重建光吸收来应对散射问题，并在医学成像、遥感及自动驾驶领域展现出重要意义。传统DOT重建范式需要以预定分辨率将物体体积离散化为体素，以模拟漫射光传播，导致重建结果的空间分辨率通常受限。我们提出NeuDOT——一种基于神经场（NF）的新型DOT方案，可连续编码体积内的光吸收，进而弥合模型精度与高分辨率之间的差距。综合实验表明，NeuDOT在14毫米深度处实现了亚毫米横向分辨率，并成功重建了复杂三维物体，性能优于现有最优方法。NeuDOT是一种非侵入式、高分辨率且计算高效的断层成像方法，开拓了涉及光散射的神经场应用的新方向。