OCTN：用于机器人引导精准干预的神经OCT表示

中文摘要 AI 辅助

光学相干断层扫描（OCT）提供紧凑、非接触、微米级成像，适用于术中引导，但原生OCT体数据是离散采样、各向异性的，目前在下游几何推理和机器人集成方面效率低下。我们提出OCTN（发音为“octane”），一种隐式神经表示框架，将体积OCT扫描转换为连续、可微分且空间保真的组织强度场。OCTN采用两阶段混合训练策略，结合来自采集体素的监督与层间插值，在保持B扫描保真度的同时改善稀疏采样区域的连续性。为增强通用性，我们首先展示OCTN通过将学习到的组织表示原生存储在GPU上，支持基于强度的空间查询，相比传统CPU处理实现高达43倍加速，从而实现快速体积推理。随后，我们演示了OCTN支持的OCT引导机器人激光手术，其中连续组织表示支持通过多种优化策略（包括基于牛顿和SGD的优化）进行隐式表面发现和表面约束路径规划。此外，OCTN能够从稀疏采集的B扫描重建密集体积结构，同时将采集时间减少4倍，并保留临床相关结构。在新生成的Duke TissueOCT数据集和公共OCT数据集上，OCTN实现了稳健的高保真重建，PSNR>30 dB，训练时间<10秒，同时相对于基线重建，表面一致性保持在10微米Chamfer距离内。TissueOCT数据集和代码可在该http URL公开获取。

英文摘要

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 $μ$m Chamfer distance relative to baseline reconstruction. The TissueOCT dataset and code are publicly available at raprakashvi.github.io/octn

发表机构

• Duke University（杜克大学）
• Arizona State University（亚利桑那州立大学）

机构由 AI 辅助整理，请以论文原文为准。