Ji Xueqin, Zhao Shuting, Liu Di, Wang Feng, Chen Xinrong
The Third School of Clinical Medicine, Ningxia Medical University, Yinchuan, China.
Department of Ultrasound, Peking University First Hospital Ningxia Women and Children's Hospital, Yinchuan, China.
Front Neurorobot. 2025 Jun 27;19:1630728. doi: 10.3389/fnbot.2025.1630728. eCollection 2025.
In nasal endoscopic surgery, the narrow nasal cavity restricts the surgical field of view and the manipulation of surgical instruments. Therefore, precise real-time intraoperative navigation, which can provide precise 3D information, plays a crucial role in avoiding critical areas with dense blood vessels and nerves. Although significant progress has been made in endoscopic 3D reconstruction methods, their application in nasal scenarios still faces numerous challenges. On the one hand, there is a lack of high-quality, annotated nasal endoscopy datasets. On the other hand, issues such as motion blur and soft tissue deformations complicate the nasal endoscopy reconstruction process. To tackle these challenges, a series of nasal endoscopy examination videos are collected, and the pose information for each frame is recorded. Additionally, a novel model named Mip-EndoGS is proposed, which integrates 3D Gaussian Splatting for reconstruction and rendering and a diffusion module to reduce image blurring in endoscopic data. Meanwhile, by incorporating an adaptive low-pass filter into the rendering pipeline, the aliasing artifacts (jagged edges) are mitigated, which occur during the rendering process. Extensive quantitative and visual experiments show that the proposed model is capable of reconstructing 3D scenes within the nasal cavity in real-time, thereby offering surgeons more detailed and precise information about the surgical scene. Moreover, the proposed approach holds great potential for integration with AR-based surgical navigation systems to enhance intraoperative guidance.
在鼻内镜手术中，狭窄的鼻腔限制了手术视野和手术器械的操作。因此，能够提供精确三维信息的精确实时术中导航在避开血管和神经密集的关键区域方面起着至关重要的作用。尽管内镜三维重建方法已经取得了显著进展，但其在鼻腔场景中的应用仍然面临诸多挑战。一方面，缺乏高质量的、带有注释的鼻内镜数据集。另一方面，运动模糊和软组织变形等问题使鼻内镜重建过程变得复杂。为了应对这些挑战，收集了一系列鼻内镜检查视频，并记录了每一帧的位姿信息。此外，还提出了一种名为Mip-EndoGS的新型模型，该模型集成了用于重建和渲染的三维高斯点渲染以及一个扩散模块，以减少内镜数据中的图像模糊。同时，通过在渲染管道中加入自适应低通滤波器，减轻了渲染过程中出现的混叠伪像（锯齿边缘）。大量的定量和视觉实验表明，所提出的模型能够实时重建鼻腔内的三维场景，从而为外科医生提供有关手术场景更详细、精确的信息。此外，所提出的方法在与基于增强现实的手术导航系统集成以增强术中引导方面具有巨大潜力。
