Positron Emission Tomography Lung Image Respiratory Motion Correcting with Equivariant Transformer  

作　　者：Jianfeng He Haowei Ye Jie Ning Hui Zhou Bo She 

机构地区：[1]Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Yunnan Key Laboratory of Artificial Intelligence,Kunming,650500,China [2]School of Physics and Electronic Engineering,Yuxi Normal University,Yuxi,653100,China [3]PET/CT Center,Affiliated Hospital of Kunming University of Science and Technology,First People’s Hospital of Yunnan Province,Kunming,650031,China

出　　处：《Computers, Materials & Continua》2024年第5期3355-3372,共18页计算机、材料和连续体（英文）

基　　金：the National Natural Science Foundation of China(No.82160347);Yunnan Provincial Science and Technology Department(No.202102AE090031);Yunnan Key Laboratory of Smart City in Cyberspace Security(No.202105AG070010).

摘　　要：In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our studyintroduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learningbasedframework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques,which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency andoveremphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective featureextraction and motion decomposition.TEMT’s unique approach involves transforming motion sequences into Liegroup domains to highlight fundamental motion patterns, coupled with employing competitive weighting forprecise target deformation field generation. Our empirical evaluations confirm TEMT’s superior performancein handling diverse PET lung datasets compared to existing image registration networks. Experimental resultsdemonstrate that TEMT achieved Dice indices of 91.40%, 85.41%, 79.78%, and 72.16% on simulated geometricphantom data, lung voxel phantom data, cardiopulmonary voxel phantom data, and clinical data, respectively. Tofacilitate further research and practical application, the TEMT framework, along with its implementation detailsand part of the simulation data, is made publicly accessible at https://github.com/yehaowei/temt.

关 键 词：PET lung scans respiratory motion correction triple equivariant motion transformer lie group motion decomposition 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] R563[自动化与计算机技术—计算机科学与技术]