Endoscopic navigation in the clinic: registration in the absence of preoperative imaging
Endoscopic navigation in the clinic: registration in the absence of preoperative imaging
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DOI：
10.1007/s11548-019-02005-0
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发表时间：
2019-09-01
影响因子：
3
通讯作者：
Taylor, Russell H.
中科院分区：
文献类型：
--
作者：
Sinha, Ayushi;Ishii, Masaru;Taylor, Russell H.
目的：临床检查涉及鼻腔和鼻窦的内窥镜探查，通常没有参考术前图像，如计算机断层扫描（CT）扫描，以提供结构背景给临床医生。这项工作的目的是在临床探索期间提供结构背景，而不需要额外的CT采集。方法我们提出了一种方法，在临床内窥镜的CT扫描的情况下，通过使用过去的CT扫描的形状统计配准。使用可变形配准算法，该可变形配准算法使用这些形状统计沿着来自视频的密集点云，我们同时实现两个目标：（1）将目标解剖结构的统计平均形状与视频点云配准，以及（2）通过使平均形状变形以拟合视频点云来估计患者形状。最后，我们使用统计测试来分配置信度的计算注册。结果我们能够实现亚毫米级的配准误差和患者形状重建使用模拟数据。我们建立和评估我们的注册使用模拟数据的置信标准。最后，我们在体内临床数据上评估我们的配准方法，并使用模拟中建立的标准为这些配准分配置信度。所有的注册，不拒绝我们的标准产生亚毫米级的残余误差。结论我们的变形配准方法可以产生亚毫米级的配准和重建，以及统计分数，可用于分配置信度的配准。
Purpose Clinical examinations that involve endoscopic exploration of the nasal cavity and sinuses often do not have a reference preoperative image, like a computed tomography (CT) scan, to provide structural context to the clinician. The aim of this work is to provide structural context during clinical exploration without requiring additional CT acquisition. Methods We present a method for registration during clinical endoscopy in the absence of CT scans by making use of shape statistics from past CT scans. Using a deformable registration algorithm that uses these shape statistics along with dense point clouds from video, we simultaneously achieve two goals: (1) register the statistically mean shape of the target anatomy with the video point cloud, and (2) estimate patient shape by deforming the mean shape to fit the video point cloud. Finally, we use statistical tests to assign confidence to the computed registration. Results We are able to achieve submillimeter errors in registrations and patient shape reconstructions using simulated data. We establish and evaluate the confidence criteria for our registrations using simulated data. Finally, we evaluate our registration method on in vivo clinical data and assign confidence to these registrations using the criteria established in simulation. All registrations that are not rejected by our criteria produce submillimeter residual errors. Conclusion Our deformable registration method can produce submillimeter registrations and reconstructions as well as statistical scores that can be used to assign confidence to the registrations.