Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada.
Department of Orthopaedics, University of British Columbia, Vancouver, Canada.
Int J Comput Assist Radiol Surg. 2018 Oct;13(10):1515-1524. doi: 10.1007/s11548-018-1788-5. Epub 2018 May 26.
Ultrasound (US) is a safer alternative to X-rays for bone imaging, and its popularity for orthopedic surgical navigation is growing. Routine use of intraoperative US for navigation requires fast, accurate and automatic alignment of tracked US to preoperative computed tomography (CT) patient models. Our group previously investigated image segmentation and registration to align untracked US to CT of only the partial pelvic anatomy. In this paper, we extend this to study the performance of these previously published techniques over the full pelvis in a tracked framework, to characterize their suitability in more realistic scenarios, along with an additional simplified segmentation method and similarity metric for registration.
We evaluated phase symmetry segmentation, and Gaussian mixture model (GMM) and coherent point drift (CPD) registration methods on a pelvic phantom augmented with human soft tissue images. Additionally, we proposed and evaluated a simplified 3D bone segmentation algorithm we call Shadow-Peak (SP), which uses acoustic shadowing and peak intensities to detect bone surfaces. We paired this with a registration pipeline that optimizes the normalized cross-correlation (NCC) between distance maps of the segmented US-CT images.
SP segmentation combined with the proposed NCC registration successfully aligned tracked US volumes to the preoperative CT model in all trials, in contrast to the other techniques. SP with NCC achieved a median target registration error (TRE) of 2.44 mm (maximum 4.06 mm), when imaging all three anterior pelvic structures, and a mean runtime of 27.3 s. SP segmentation with CPD registration was the next most accurate combination: median TRE of 3.19 mm (maximum 6.07 mm), though a much faster runtime of 4.2 s.
We demonstrate an accurate, automatic image processing pipeline for intraoperative alignment of US-CT over the full pelvis and compare its performance with the state-of-the-art methods. The proposed methods are amenable to clinical implementation due to their high accuracy on realistic data and acceptably low runtimes.
超声（US）是一种比 X 射线更安全的骨成像替代方法，其在骨科手术导航中的应用越来越广泛。常规使用术中 US 进行导航需要快速、准确和自动将跟踪的 US 与术前计算机断层扫描（CT）患者模型对齐。我们的团队之前研究了图像分割和配准，以将未跟踪的 US 与仅部分骨盆解剖结构的 CT 对齐。在本文中，我们将这一方法扩展到了在跟踪框架中对整个骨盆进行研究，以表征其在更现实场景中的适用性，同时还提出了一种简化的分割方法和用于配准的相似性度量。
我们评估了相位对称分割、高斯混合模型（GMM）和相干点漂移（CPD）配准方法在增强了人体软组织图像的骨盆模型上的性能。此外，我们提出并评估了一种简化的 3D 骨骼分割算法，我们称之为 Shadow-Peak（SP），它使用声影和峰值强度来检测骨骼表面。我们将其与一个注册管道配对，该管道优化了分割 US-CT 图像的距离图之间的归一化互相关（NCC）。
SP 分割与所提出的 NCC 注册相结合，在所有试验中都成功地将跟踪的 US 体积与术前 CT 模型对齐，与其他技术形成对比。当对所有三个前骨盆结构进行成像时，SP 与 NCC 实现了中位数目标注册误差（TRE）为 2.44mm（最大 4.06mm），平均运行时间为 27.3s。SP 分割与 CPD 注册是下一个最准确的组合：中位数 TRE 为 3.19mm（最大 6.07mm），尽管运行时间快得多，为 4.2s。
我们展示了一种用于全骨盆术中 US-CT 自动配准的准确、自动图像处理管道，并将其性能与最先进的方法进行了比较。由于其在真实数据上的高精度和可接受的低运行时间，所提出的方法适合临床实施。
