将OCT影像转为连续可微的神经表示，提升机器人手术精度与成像效率
OCTN: Neural OCT Representations for Robot-Guided Precision Intervention
- 用双阶段训练将离散OCT数据转为连续组织强度场
- 重建速度提升43倍，采样减少4倍且保持10μm内表面一致性
- 支持机器人路径规划与稀疏扫描重建，适合微创手术应用
光学相干断层扫描（OCT）提供无接触、微米级成像，适用于术中导航，但原始OCT体积存在离散采样、各向异性问题，难以高效用于几何推理和机器人集成。我们提出OCTN（读作“octane”），一种隐式神经表示框架，将体素OCT扫描转化为连续、可微、空间保真的组织强度场。OCTN采用两阶段混合训练策略，结合已获取体素监督与跨切片插值，保留B-scan保真度并提升稀疏区域连续性。实验表明，OCTN可在GPU上原生存储学习到的组织表示，实现基于强度的空间查询，相比传统CPU处理提速达43倍。进一步展示其在OCT引导机器人激光手术中的应用：连续组织表示支持隐式表面发现与表面约束路径规划，通过牛顿法和SGD等优化策略实现。此外，OCTN能从稀疏采集的B-scan重建密集体积结构，使采集时间减少4倍，同时保留临床相关结构。在新构建的Duke TissueOCT数据集及公开OCT数据集上，OCTN实现高保真重建（PSNR > 30 dB），训练时间小于10秒，表面一致性保持在10 μm Chamfer距离内。代码与数据集已开源。
原文摘要 · Abstract (English)
Optical coherence tomography (OCT) offers compact, contactless, micron-scale imaging suitable for intraoperative guidance, but native OCT volumes are discretely sampled, anisotropic, and currently inefficient for downstream geometric reasoning and robot integration. We present OCTN (pronounced "octane"), an implicit neural representation framework that converts volumetric OCT scans into a continuous, differentiable, and spatially faithful tissue-intensity field. OCTN uses a two-stage hybrid training strategy that combines supervision from acquired voxels with inter-slice interpolations, preserving B-scan fidelity while improving continuity in sparsely sampled regions. For versatility, we first show that OCTN enables fast volumetric reasoning by storing the learned tissue representation natively on the GPU, supporting intensity-based spatial queries with up to 43x speedup over conventional CPU processing. We then demonstrate OCTN-enabled OCT-guided robotic laser surgery where the continuous tissue representation supports implicit surface discovery and surface-constrained path planning via multiple optimization strategies, including Newton- and SGD-based optimization. Next, OCTN enables reconstruction of dense volumetric structure from sparsely acquired B-scans, while reducing acquisition time by 4x and preserving clinically relevant structures. Across the newly generated Duke TissueOCT dataset and public OCT datasets, OCTN achieves robust, high-fidelity reconstruction with PSNR > 30 dB and training time < 10 s, while preserving surface consistency within 10 $\mu$m Chamfer distance relative to baseline reconstruction. The TissueOCT dataset and code are publicly available at raprakashvi.github.io/octn
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