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-learningbasedfr...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.展开更多
Hepatocellular carcinoma(HCC)is one of the leading causes of cancer mortality worldwide.Various imaging modalities provide important information about HCC for its clinical management.Since positron-emission tomography...Hepatocellular carcinoma(HCC)is one of the leading causes of cancer mortality worldwide.Various imaging modalities provide important information about HCC for its clinical management.Since positron-emission tomography(PET)or PET-computed tomography was introduced to the oncologic setting,it has played crucial roles in detecting,distinguishing,accurately staging,and evaluating local,residual,and recurrent HCC.PET imaging visualizes tissue metabolic information that is closely associated with treatment.Dynamic PET imaging and dual-tracer have emerged as complementary techniques that aid in various aspects of HCC diagnosis.The advent of new radiotracers and the development of immuno-PET and PET-magnetic resonance imaging have improved the ability to detect lesions and have made great progress in treatment surveillance.The current PET diagnostic capabilities for HCC and the supplementary techniques are reviewed herein.展开更多
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).
文摘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.
基金Supported by the National Natural Science Foundation of China,No.81760306the Basic Research on Application of Joint Special Funding of Science and Technology Department of Yunnan Province-Kunming Medical University,No.2018FE001(-291)
文摘Hepatocellular carcinoma(HCC)is one of the leading causes of cancer mortality worldwide.Various imaging modalities provide important information about HCC for its clinical management.Since positron-emission tomography(PET)or PET-computed tomography was introduced to the oncologic setting,it has played crucial roles in detecting,distinguishing,accurately staging,and evaluating local,residual,and recurrent HCC.PET imaging visualizes tissue metabolic information that is closely associated with treatment.Dynamic PET imaging and dual-tracer have emerged as complementary techniques that aid in various aspects of HCC diagnosis.The advent of new radiotracers and the development of immuno-PET and PET-magnetic resonance imaging have improved the ability to detect lesions and have made great progress in treatment surveillance.The current PET diagnostic capabilities for HCC and the supplementary techniques are reviewed herein.
