Unsupervised 3D PET-CT Image Registration Method Using a Metabolic Constraint Function and a Multi-Domain Similarity Measure
Unsupervised 3D PET-CT Image Registration Method Using a Metabolic Constraint Function and a Multi-Domain Similarity Measure
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使用代谢约束函数和多域相似性测量的无监督 3D PET-CT 图像配准方法
DOI：
10.1109/access.2020.2984804
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Hiroshi Fujita
中科院分区：
文献类型：
--
作者：
Hengjian Yu;Huiyan Jiang;Xiangrong Zhou;Takeshi Hara;Yu-dong Yao;Hiroshi Fujita
High-resolution CT images can clearly display anatomical structures but does not display functional information, while blurred PET images can display molecular and functional information of lesions but cannot clearly display morphological structures. Therefore, accurate PET-CT image registration, which is used for anatomical structure and functional information fusion, is a prerequisite for early stage cancer diagnosis. However, some hypermetabolic anatomical structures, such as brain and bladder, have low registration accuracy. To solve this problem, a 3D unsupervised network based on a metabolic constraint function and a multi-domain similarity measure (3D MC-MDS Net) is proposed for 3D PET-CT image registration. Specifically, a metabolic constraint model is established based on the standard uptake value (SUV) distribution of hypermetabolic regions such as brain, bladder, liver and heart, which reduces the excessive distortion on displacement vector field (DVF) caused by hypermetabolic anatomical structures in PET images. A DVF estimator is built based on 3D unsupervised convolutional neural networks and a spatial transformer is used for warping 3D PET images to 3D CT images. The generated registration results (PET image patches) and the original 3D CT image patches are used for calculating the spatial domain similarity (SD similarity) and frequency domain similarity (FD similarity). Finally, the loss function of the entire registration network is constructed by a weighted sum of SD similarity, FD similarity and a smoothness of DVF. A dataset consisted of 170 whole-body PET-CT images is used for registration accuracy evaluation. The proposed unsupervised registration network, 3D MC-MDS Net, can accurately learn the 3D registration model by using the training dataset with the metabolic constraint model, which significantly improves the registration accuracy.
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DOI：
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发表时间：
2003-08
期刊：
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
影响因子：
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作者：
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通讯作者：
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2002-03-01
影响因子：
9.1
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影响因子：
5.7
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DOI：
10.1109/tbme.2015.2496253
发表时间：
2016-07
期刊：
IEEE transactions on bio-medical engineering
影响因子：
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作者：
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通讯作者：
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