Statistical Shape Model to 3D Ultrasound Registration for Spine Interventions Using Enhanced Local Phase Features
Statistical Shape Model to 3D Ultrasound Registration for Spine Interventions Using Enhanced Local Phase Features
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使用增强局部相位特征进行脊柱干预的 3D 超声配准的统计形状模型
DOI：
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发表时间：
2013
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通讯作者：
P. Abolmaesumi
中科院分区：
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作者：
I. Hacihaliloglu;Abtin Rasoulian;R. Rohling;P. Abolmaesumi
由于超声固有的典型成像伪影，将超声图像准确配准到统计形状模型是经皮脊柱注射手术中的一个具有挑战性的问题。在本文中，我们提出了一种稳健且准确的配准方法，将从超声图像中提取的局部相骨特征与统计形状模型相匹配。使用梯度能量张量滤波器获得用于增强骨表面的局部相位信息，该滤波器结合了单基因尺度空间滤波器和高斯尺度空间滤波器的优点，从而改进了相位和方向信息的同时估计。通过将姿态统计数据与形状统计数据分离来建立一种新颖的统计形状模型。然后使用迭代期望最大化配准技术将该模型配准到局部相位骨表面。对从 8 名患者获得的 96 次体内临床扫描进行验证，均方根配准误差为 2 毫米（SD：0.4 毫米），低于临床可接受的阈值 3.5 毫米。与最先进的局部相位图像处理方法相比，使用新功能在配准精度方面实现的改进也很显着 (p < 0.05)。
Accurate registration of ultrasound images to statistical shape models is a challenging problem in percutaneous spine injection procedures due to the typical imaging artifacts inherent to ultrasound. In this paper we propose a robust and accurate registration method that matches local phase bone features extracted from ultrasound images to a statistical shape model. The local phase information for enhancing the bone surfaces is obtained using a gradient energy tensor filter, which combines advantages of the monogenic scale-space and Gaussian scale-space filters, resulting in an improved simultaneous estimation of phase and orientation information. A novel statistical shape model was built by separating the pose statistics from the shape statistics. This model is then registered to the local phase bone surfaces using an iterative expectation maximization registration technique. Validation on 96 in vivo clinical scans obtained from eight patients resulted in a root mean square registration error of 2 mm (SD: 0.4 mm), which is below the clinically acceptable threshold of 3.5 mm. The improvement achieved in registration accuracy using the new features was also significant (p < 0.05) compared to state of the art local phase image processing methods.