Biomechanically constrained groupwise ultrasound to CT registration of the lumbar spine
Biomechanically constrained groupwise ultrasound to CT registration of the lumbar spine
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DOI：
10.1016/j.media.2010.07.008
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发表时间：
2012-04-01
影响因子：
10.9
通讯作者：
Mousavi, Parvin
中科院分区：
文献类型：
--
作者：
Gill, Sean;Abolmaesumi, Purang;Mousavi, Parvin
我们提出了一种分组US到CT配准算法，用于指导经皮脊柱介入治疗。此外，我们还介绍了一个综合的验证方案，该方案考虑了术前和术中成像之间脊柱曲率的变化。在我们的配准方法中，CT中的每个椎骨被视为一个子体积，并单独转换。生物力学模型用于约束椎骨相对于彼此的位移。然后将子体积重建成单个体积。在配准的每次迭代期间，根据重建的CT体积模拟US图像，并且利用真实的US图像计算基于强度的相似性度量。验证研究是在一具羊尸体的CT和US图像上进行的，五个基于患者的体模设计用于保持脊柱的真实曲率，第六个基于患者的体模在术前和术中成像之间改变脊柱的曲率。对于两种成像模式之间的脊柱曲线被人为扰动的数据集，所提出的方法能够以95%的成功率记录高达20 mm的初始未对准。对于在US和CT数据集之间引入脊柱曲率物理变化的体模，配准成功率为98.5%。最后，对具有软组织信息的羊尸的配准成功率为87%。结果表明，无论术前和术中图像采集之间患者姿势的变化如何，我们的算法都可以实现US和CT数据集的稳健配准。(C)2010 Elsevier B.V.保留所有权利。
We present a groupwise US to CT registration algorithm for guiding percutaneous spinal interventions. In addition, we introduce a comprehensive validation scheme that accounts for changes in the curvature of the spine between preoperative and intraoperative imaging. In our registration methodology, each vertebra in CT is treated as a sub-volume and transformed individually. A biomechanical model is used to constrain the displacement of the vertebrae relative to one another. The sub-volumes are then reconstructed into a single volume. During each iteration of registration, an US image is simulated from the reconstructed CT volume and an intensity-based similarity metric is calculated with the real US image. Validation studies are performed on CT and US images from a sheep cadaver, five patient-based phantoms designed to preserve realistic curvatures of the spine and a sixth patient-based phantom where the curvature of the spine is changed between preoperative and intraoperative imaging.For datasets where the spine curve between two imaging modalities was artificially perturbed, the proposed methodology was able to register initial misalignments of up to 20 mm with a success rate of 95%. For the phantom with a physical change in the curvature of the spine introduced between the US and CT datasets, the registration success rate was 98.5%. Finally, the registration success rate for the sheep cadaver with soft-tissue information was 87%. The results demonstrate that our algorithm allows for robust registration of US and CT datasets, regardless of a change in the patients pose between preoperative and intraoperative image acquisitions. (C) 2010 Elsevier B.V. All rights reserved.