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首页> 美国卫生研究院文献>Journal of Applied Clinical Medical Physics >Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients
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Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients

机译：基于互信息魔鬼多分辨率梯度算法的PET和CT图像配准对食管癌患者的定位

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Accurate registration of  18F−FDG PET (positron emission tomography) and CT (computed tomography) images has important clinical significance in radiation oncology. PET and CT images are acquired from  18F−FDG PET/CT scanner, but the two acquisition processes are separate and take a long time. As a result, there are position errors in global and deformable errors in local caused by respiratory movement or organ peristalsis. The purpose of this work was to implement and validate a deformable CT to PET image registration method in esophageal cancer to eventually facilitate accurate positioning the tumor target on CT, and improve the accuracy of radiation therapy. Global registration was firstly utilized to preprocess position errors between PET and CT images, achieving the purpose of aligning these two images on the whole. Demons algorithm, based on optical flow field, has the features of fast process speed and high accuracy, and the gradient of mutual information‐based demons (GMI demons) algorithm adds an additional external force based on the gradient of mutual information (GMI) between two images, which is suitable for multimodality images registration. In this paper, GMI demons algorithm was used to achieve local deformable registration of PET and CT images, which can effectively reduce errors between internal organs. In addition, to speed up the registration process, maintain its robustness, and avoid the local extremum, multiresolution image pyramid structure was used before deformable registration. By quantitatively and qualitatively analyzing cases with esophageal cancer, the registration scheme proposed in this paper can improve registration accuracy and speed, which is helpful for precisely positioning tumor target and developing the radiation treatment planning in clinical radiation therapy application.PACS numbers: 87.57.nj, 87.57.Q‐, 87.57.uk
机译：准确注册 18 F − FDG < mo> PET （正电子发射断层扫描）和CT（计算机断层扫描）图像在放射肿瘤学中具有重要的临床意义。 PET和CT图像是从 18 F − FDG < / mtext> PET / CT扫描仪，但是这两个采集过程是分开的，需要很长时间。结果，由呼吸运动或器官蠕动引起的整体位置误差和局部变形误差。这项工作的目的是在食管癌中实施和验证可变形CT到PET图像配准方法，以最终促进将肿瘤靶标精确定位在CT上，并提高放射治疗的准确性。首先利用全局配准对PET和CT图像之间的位置误差进行预处理，以达到将这两个图像整体对齐的目的。基于光流场的恶魔算法具有处理速度快，精度高的特点，基于互信息梯度的恶魔（GMI demons）算法基于互信息梯度（GMI）增加了额外的外力。两个图像，适合多模态图像配准。本文采用GMI恶魔算法实现PET和CT图像的局部可变形配准，可有效减少内部器官之间的误差。另外，为了加快配准过程，保持其鲁棒性并避免局部极值，在可变形配准之前使用了多分辨率图像金字塔结构。通过对食管癌病例进行定量和定性分析，本文提出的配准方案可以提高配准的准确性和速度，有助于精确定位肿瘤靶点并制定临床放射治疗应用中的放射治疗计划。PACS编号：87.57.nj ，87.57.Q‐，87.57.uk

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