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A Stationary Wavelet Transform Based Approach to Registration of Planning CT and Setup Cone beam-CT Images in Radiotherapy

机译:基于平稳小波变换的放射线治疗计划CT和建立锥形束CT图像配准方法

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摘要

Image registration between planning CT images and cone beam-CT (CBCT) images is one of the key technologies of image guided radiotherapy (IGRT). Current image registration methods fall roughly into two categories: geometric features-based and image grayscale-based. Mutual information (MI) based registration, which belongs to the latter category, has been widely applied to multi-modal and mono-modal image registration. However, the standard mutual information method only focuses on the image intensity information and overlooks spatial information, leading to the instability of intensity interpolation. Due to its use of positional information, wavelet transform has been applied to image registration recently. In this study, we proposed an approach to setup CT and cone beam-CT (CBCT) image registration in radiotherapy based on the combination of mutual information (MI) and stationary wavelet transform (SWT). Firstly, SWT was applied to generate gradient images and low frequency components produced in various levels of image decomposition were eliminated. Then inverse SWT was performed on the remaining frequency components. Lastly, the rigid registration of gradient images and original images was implemented using a weighting function with the normalized mutual information (NMI) being the similarity measure, which compensates for the lack of spatial information in mutual information based image registration. Our experiment results showed that the proposed method was highly accurate and robust, and indicated a significant clinical potential in improving the accuracy of target localization in image guided radiotherapy (IGRT).
机译:计划的CT图像和锥束CT(CBCT)图像之间的图像配准是图像引导放射治疗(IGRT)的关键技术之一。当前的图像配准方法大致分为两类:基于几何特征和基于图像灰度。基于互信息(MI)的配准属于后一类,已被广泛应用于多模式和单模式图像配准。然而,标准的互信息方法仅关注图像强度信息而忽略空间信息,从而导致强度插值的不稳定。由于使用了位置信息,因此小波变换最近已应用于图像配准。在这项研究中,我们提出了一种基于互信息(MI)和平稳小波变换(SWT)组合的放射治疗中CT和锥束CT(CBCT)图像配准的方法。首先,将SWT应用于生成梯度图像,并消除了在各种图像分解级别中产生的低频分量。然后,对其余频率分量执行逆SWT。最后,使用加权函数实现梯度图像和原始图像的刚性配准,其中归一化互信息(NMI)是相似性度量,这可以弥补基于互信息的图像配准中缺乏空间信息。我们的实验结果表明,该方法具有很高的准确性和鲁棒性,并在提高影像引导放疗(IGRT)中靶标定位的准确性方面具有重要的临床潜力。

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