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Volumetric mosaicing for optical coherence tomography for large area bladder wall visualization

机译：体积镶嵌技术用于光学相干断层扫描，用于大面积膀胱壁可视化

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Optical coherence tomography (OCT) has shown potential as a complementary imaging modality to white light cystoscopy (WLC) because it can visualize sub-surface details of the bladder wall, enabling it to stage early cancers and visualize tumors undetectable to WLC. However, the inherently small field of view (FOV) of OCT compared with the area of the bladder wall restricts its clinical utility. A large OCT FOV could improve surgical planning by enabling complete visualization of tumor margins or could aid in early cancer detection by tracking the appearance of the bladder wall over time. To overcome the limited FOV of OCT. we developed a method to create mosaics of OCT volume data using a modified version of the N-dimensional scale invariant feature transform (N-SIFT) algorithm: white-light-enhanced N-SIFT (WhiLE-NS). WhiLE-NS adds a pre-processing step to N-SIFT that uses white light images co-registered with OCT volumes to select small, highly overlapped volumes on which to run N-SIFT. This pre-processing step adds minimal computational time and enables a 200-fold decrease in the amount of time required to register two volumes compared with N-SIFT alone. Quantitatively. WhiLE-NS achieves nearly sub-pixel registration accuracy, and qualitatively, we demonstrate that the algorithm can generate large FOV mosaics of ex vivo bladder tissue. The realization of this algorithm is a critical step to enabling OCT to contribute meaningfully to bladder surveillance and surgical guidance.
机译：光学相干断层扫描（OCT）已显示出作为白光膀胱镜检查（WLC）的补充成像方式的潜力，因为它可以可视化膀胱壁的亚表面细节，使其能够分期早期癌症并可视化WLC无法检测到的肿瘤。但是，与膀胱壁面积相比，OCT本质上较小的视野（FOV）限制了其临床实用性。较大的OCT FOV可以通过实现肿瘤边缘的完全可视化来改善手术计划，或者可以通过跟踪一段时间内膀胱壁的出现来帮助早期癌症检测。克服OCT的有限FOV。我们开发了一种使用N维尺度不变特征变换（N-SIFT）算法的改进版本创建OCT体数据镶嵌图的方法：白光增强N-SIFT（WhiLE-NS）。 WhiLE-NS在N-SIFT中增加了预处理步骤，该步骤使用与OCT体积共配准的白光图像来选择小的，高度重叠的体积，以在其上运行N-SIFT。与仅使用N-SIFT相比，此预处理步骤可节省最少的计算时间，并使注册两个卷所需的时间减少200倍。数量上。 WhiLE-NS几乎达到了亚像素配准精度，并且从质量上讲，我们证明了该算法可以生成离体膀胱组织的大FOV镶嵌图。该算法的实现是使OCT能够为膀胱监视和手术指导做出有意义的贡献的关键步骤。

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