背景:正电子发射计算机断层显像(PET)从分子水平反映细胞代谢和功能变化,MRI有更高的软组织对比度和空间分辨率,且无电磁辐射。近年来,随着多模式成像技术飞速发展,作为刚刚应用于临床的多模式分子成像技术,PET/MRI融合显像可提供分子...背景:正电子发射计算机断层显像(PET)从分子水平反映细胞代谢和功能变化,MRI有更高的软组织对比度和空间分辨率,且无电磁辐射。近年来,随着多模式成像技术飞速发展,作为刚刚应用于临床的多模式分子成像技术,PET/MRI融合显像可提供分子、形态与功能信息,有关研究已成为分子影像领域的关注焦点。目的:就目前PET/MRI研制存在的问题和进展情况做一综述,并展望其潜在的临床和科研价值。方法:由第一作者应用计算机检索CNKI数据库、Springerlink数据库、Pubmed数据库相关文献。检索时间范围:2000年1月至2012年6月。英文检索词:"PET/MRI"和"multimodality imaging or image fusion",中文检索词:"分子影像学"和"图像融合"。选择内容与PET/MRI多模式成像相关的文章,同一领域文献则选择近期发表或发表在权威杂志文章,共纳入53篇文献。结果与结论:已有的研究成果表明,PET/MRI较传统显像手段和其他成功的多模式成像技术,如PET/CT和SPECT/CT有更多、更新的应用优势,并已积累临床前和初步临床应用的经验,PET/MRI具有广阔的发展空间,有望将分子影像技术的发展带入一个新的时代,为临床诊断掀开新的篇章。展开更多
Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to ...Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition,the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision tasks.This study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named Super Fusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion,and semantic segmentation tasks demonstrate the superiority of our Super Fusion compared to the state-of-the-art alternatives.The source code and pre-trained model are publicly available at https://github.com/Linfeng-Tang/Super Fusion.展开更多
文摘背景:正电子发射计算机断层显像(PET)从分子水平反映细胞代谢和功能变化,MRI有更高的软组织对比度和空间分辨率,且无电磁辐射。近年来,随着多模式成像技术飞速发展,作为刚刚应用于临床的多模式分子成像技术,PET/MRI融合显像可提供分子、形态与功能信息,有关研究已成为分子影像领域的关注焦点。目的:就目前PET/MRI研制存在的问题和进展情况做一综述,并展望其潜在的临床和科研价值。方法:由第一作者应用计算机检索CNKI数据库、Springerlink数据库、Pubmed数据库相关文献。检索时间范围:2000年1月至2012年6月。英文检索词:"PET/MRI"和"multimodality imaging or image fusion",中文检索词:"分子影像学"和"图像融合"。选择内容与PET/MRI多模式成像相关的文章,同一领域文献则选择近期发表或发表在权威杂志文章,共纳入53篇文献。结果与结论:已有的研究成果表明,PET/MRI较传统显像手段和其他成功的多模式成像技术,如PET/CT和SPECT/CT有更多、更新的应用优势,并已积累临床前和初步临床应用的经验,PET/MRI具有广阔的发展空间,有望将分子影像技术的发展带入一个新的时代,为临床诊断掀开新的篇章。
基金supported by the National Natural Science Foundation of China(62276192,62075169,62061160370)the Key Research and Development Program of Hubei Province(2020BAB113)。
文摘Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition,the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision tasks.This study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named Super Fusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion,and semantic segmentation tasks demonstrate the superiority of our Super Fusion compared to the state-of-the-art alternatives.The source code and pre-trained model are publicly available at https://github.com/Linfeng-Tang/Super Fusion.
