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
Objective To observe the clinical application value of total free-breathing cardiac MR(CMR)examination preliminarily.Methods Two patients who underwent CMR scanning under free-breathing state,including cine,motion cor...Objective To observe the clinical application value of total free-breathing cardiac MR(CMR)examination preliminarily.Methods Two patients who underwent CMR scanning under free-breathing state,including cine,motion correction T1 and T2 mapping,blood flow imaging,and late gadolinium enhancement scanning were retrospectively enrolled,and the qualities of the above images were evaluated and compared with that of conventional CMR images under breath-holding state.Results No significant difference of imaging quality was found between total free-breathing and conventional breath-holding CMR.The differences of left ventricular ejection fraction,cardiac output,left ventricular end-diastolic volume index and left ventricular mass measured based on CMR images under different breath conditions were limited.Conclusion Total free-breathing CMR was feasible in clinical practice,which could provide"one-stop"evaluation of cardiac structure,function and myocardial histological characteristics,hence having promising clinical prospects.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.
文摘Objective To observe the clinical application value of total free-breathing cardiac MR(CMR)examination preliminarily.Methods Two patients who underwent CMR scanning under free-breathing state,including cine,motion correction T1 and T2 mapping,blood flow imaging,and late gadolinium enhancement scanning were retrospectively enrolled,and the qualities of the above images were evaluated and compared with that of conventional CMR images under breath-holding state.Results No significant difference of imaging quality was found between total free-breathing and conventional breath-holding CMR.The differences of left ventricular ejection fraction,cardiac output,left ventricular end-diastolic volume index and left ventricular mass measured based on CMR images under different breath conditions were limited.Conclusion Total free-breathing CMR was feasible in clinical practice,which could provide"one-stop"evaluation of cardiac structure,function and myocardial histological characteristics,hence having promising clinical prospects.
