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首页> 外文期刊>Journal of applied clinical medical physics / >Evaluation of GMI and PMI diffeomorphic‐based demons algorithms for aligning PET and CT Images
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Evaluation of GMI and PMI diffeomorphic‐based demons algorithms for aligning PET and CT Images

机译:评估用于对齐PET和CT图像的GMI和PMI变态恶魔算法

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Fusion of anatomic information in computed tomography (CT) and functional information in F 18 ‐ FDG positron emission tomography (PET) is crucial for accurate differentiation of tumor from benign masses, designing radiotherapy treatment plan and staging of cancer. Although current PET and CT images can be acquired from combined F 18 ‐ FDG PET/CT scanner, the two acquisitions are scanned separately and take a long time, which may induce potential positional errors in global and local caused by respiratory motion or organ peristalsis. So registration (alignment) of whole-body PET and CT images is a prerequisite for their meaningful fusion. The purpose of this study was to assess the performance of two multimodal registration algorithms for aligning PET and CT images. The proposed gradient of mutual information (GMI)-based demons algorithm, which incorporated the GMI between two images as an external force to facilitate the alignment, was compared with the point-wise mutual information (PMI) diffeomorphic-based demons algorithm whose external force was modified by replacing the image intensity difference in diffeomorphic demons algorithm with the PMI to make it appropriate for multimodal image registration. Eight patients with esophageal cancer(s) were enrolled in this IRB-approved study. Whole-body PET and CT images were acquired from a combined F 18 ‐ FDG PET/CT scanner for each patient. The modified Hausdorff distance ( d MH ) was used to evaluate the registration accuracy of the two algorithms. Of all patients, the mean values and standard deviations (SDs) of d MH were 6.65 ( ± 1.90 ) voxels and 6.01 ( ± 1.90 ) after the GMI-based demons and the PMI diffeomorphic-based demons registration algorithms respectively. Preliminary results on oncological patients showed that the respiratory motion and organ peristalsis in PET/CT esophageal images could not be neglected, although a combined F 18 ‐ FDG PET/CT scanner was used for image acquisition. The PMI diffeomorphic-based demons algorithm was more accurate than the GMI-based demons algorithm in registering PET/CT esophageal images.PACS numbers: 87.57.nj, 87.57. Q-, 87.57.uk
机译:计算机断层扫描(CT)中的解剖信息与F 18-FDG正电子发射断层扫描(PET)中的功能信息的融合对于准确区分肿瘤与良性肿块,设计放射治疗方案和癌症分期至关重要。尽管可以从组合的F 18-FDG PET / CT扫描仪中获取当前的PET和CT图像,但两次采集将分别进行扫描并花费很长时间,这可能会引起由呼吸运动或器官蠕动引起的整体和局部潜在位置错误。因此,全身PET和CT图像的配准(对齐)是它们有意义融合的前提。这项研究的目的是评估两种多模式配准算法在对齐PET和CT图像方面的性能。提出的基于互信息(GMI)的恶魔梯度算法将两张图像之间的GMI作为外力合并在一起,以利于对齐,并与基于点互信息(PMI)形变的恶魔算法进行了比较。通过用PMI替换微分恶魔算法中的图像强度差异来修改它,使其适合于多模式图像配准。这项IRB批准的研究招募了8名食道癌患者。通过组合的F 18-FDG PET / CT扫描仪为每位患者获取全身PET和CT图像。修改后的Hausdorff距离(d MH)用于评估两种算法的配准精度。在所有患者中,在基于GMI的恶魔和基于PMI变态的恶魔注册算法之后,d MH的平均值和标准差(SD)分别为6.65(±1.90)体素和6.01(±1.90)。肿瘤患者的初步结果显示,尽管使用了F 18-FDG PET / CT组合扫描仪进行图像采集,但不能忽略PET / CT食管图像中的呼吸运动和器官蠕动。在配准PET / CT食管图像时,基于PMI变态的恶魔算法比基于GMI的恶魔算法更准确.PACS编号:87.57.nj,87.57。 Q-,87.57.uk

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