2D/3D Image Registration using Regression Learning.
2D/3D Image Registration using Regression Learning.
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DOI：
10.1016/j.cviu.2013.02.009
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发表时间：
2013-09-01
影响因子：
4.5
通讯作者：
Pizer, Stephen
中科院分区：
文献类型：
--
作者：
Chou, Chen-Rui;Frederick, Brandon;Mageras, Gig;Chang, Sha;Pizer, Stephen
在计算机视觉和图像分析中，实现高精度和近实时计算的 2D 投影和 3D 图像之间的图像配准具有挑战性。在本文中，我们提出了一种新方法，可以从 2D 投影图像或其一小组中快速检测对象的 3D 刚性运动或变形。该方法称为 CLARET（通过外部束治疗中的有限角度残差进行校正），由两个阶段组成：形状空间配准和回归学习。在配准阶段，线性算子用于基于目标投影和估计 3D 图像的数字重建射线照片 (DRR) 之间的当前强度残差来迭代估计运动/变形参数。该方法通过两步学习过程确定线性算子。首先，它根据先前的 3D 图像构建图像区域运动/变形形状空间的低阶参数模型。其次，使用从 3D 图像生成的学习时间样本，通过多尺度线性回归制定模型参数与共变 2D 投影强度残差之间的关系。计算出的多尺度回归矩阵产生从粗到细的线性算子，用于根据配准中的 2D 投影强度残差估计模型参数。该方法应用于图像引导放射治疗 (IGRT) 只需几秒钟，并且使用一个治疗时间成像 2D 投影或一小组治疗时间成像 2D 投影，在头部和颈部的刚性运动以及肺部呼吸变形的情况下定位肿瘤时产生良好的结果。
In computer vision and image analysis, image registration between 2D projections and a 3D image that achieves high accuracy and near real-time computation is challenging. In this paper, we propose a novel method that can rapidly detect an object’s 3D rigid motion or deformation from a 2D projection image or a small set thereof. The method is called CLARET (Correction via Limited-Angle Residues in External Beam Therapy) and consists of two stages: registration preceded by shape space and regression learning. In the registration stage, linear operators are used to iteratively estimate the motion/deformation parameters based on the current intensity residue between the target projec-tion(s) and the digitally reconstructed radiograph(s) (DRRs) of the estimated 3D image. The method determines the linear operators via a two-step learning process. First, it builds a low-order parametric model of the image region’s motion/deformation shape space from its prior 3D images. Second, using learning-time samples produced from the 3D images, it formulates the relationships between the model parameters and the co-varying 2D projection intensity residues by multi-scale linear regressions. The calculated multi-scale regression matrices yield the coarse-to-fine linear operators used in estimating the model parameters from the 2D projection intensity residues in the registration. The method’s application to Image-guided Radiation Therapy (IGRT) requires only a few seconds and yields good results in localizing a tumor under rigid motion in the head and neck and under respiratory deformation in the lung, using one treatment-time imaging 2D projection or a small set thereof.
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DOI：
10.1023/b:visi.0000043755.93987.aa
发表时间：
2005-02-01
影响因子：
19.5
作者：
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影响因子：
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