Radiation dose reduction in computed tomography(CT)can be achieved by decreasing the number of projections.However,reconstructing CT images via filtered back projection algorithm from sparse-view projections often con...Radiation dose reduction in computed tomography(CT)can be achieved by decreasing the number of projections.However,reconstructing CT images via filtered back projection algorithm from sparse-view projections often contains severe streak artifacts,affecting clinical diagnosis.To address this issue,this paper proposes TransitNet,an iterative unrolling deep neural network that combines model-driven data consistency,a physical a prior constraint,with deep learning’s feature extraction capabilities.TransitNet employs a novel iterative architecture,implementing flexible physical constraints through learnable data consistency operations,utilizing Transformer’s self-attention mechanism to model long-range dependencies in image features,and introducing linear attention mechanisms to reduce self-attention’s computational complexity from quadratic to linear.Extensive experiments demonstrate that this method exhibits significant advantages in both reconstruction quality and computational efficiency,effectively suppressing streak artifacts while preserving structures and details of images.展开更多
Grating-based X-ray phase-contrast imaging enhances the contrast of imaged objects,particularly soft tissues.However,the radiation dose in computed tomography(CT)is generally excessive owing to the complex collection ...Grating-based X-ray phase-contrast imaging enhances the contrast of imaged objects,particularly soft tissues.However,the radiation dose in computed tomography(CT)is generally excessive owing to the complex collection scheme.Sparse-view CT collection reduces the radiation dose,but with reduced resolution and reconstructed artifacts particularly in analytical reconstruction methods.Recently,deep learning has been employed in sparse-view CT reconstruction and achieved stateof-the-art results.Nevertheless,its low generalization performance and requirement for abundant training datasets have hindered the practical application of deep learning in phase-contrast CT.In this study,a CT model was used to generate a substantial number of simulated training datasets,thereby circumventing the need for experimental datasets.By training a network with simulated training datasets,the proposed method achieves high generalization performance in attenuationbased CT and phase-contrast CT,despite the lack of sufficient experimental datasets.In experiments utilizing only half of the CT data,our proposed method obtained an image quality comparable to that of the filtered back-projection algorithm with full-view projection.The proposed method simultaneously addresses two challenges in phase-contrast three-dimensional imaging,namely the lack of experimental datasets and the high exposure dose,through model-driven deep learning.This method significantly accelerates the practical application of phase-contrast CT.展开更多
目的探究低剂量多层螺旋CT(MSCT)与X线在新生儿呼吸窘迫综合征(NRDS)诊断中的应用价值。方法收集2019年1月至2024年2月我院收治的100例NRDS的病例资料,均接受MSCT及X线检查,观察其MSCT及X线特点,评估其两者对NRDS的诊断效能。结果(1)100...目的探究低剂量多层螺旋CT(MSCT)与X线在新生儿呼吸窘迫综合征(NRDS)诊断中的应用价值。方法收集2019年1月至2024年2月我院收治的100例NRDS的病例资料,均接受MSCT及X线检查,观察其MSCT及X线特点,评估其两者对NRDS的诊断效能。结果(1)100例NRDS患儿的MSCT图像中图像质量优42例(42.00%),图像质量良37例(37.00%),图像质量合格20例(20.00%),1例患儿图像质量不合格,图像质量合格率为99.00%,经镇静后重新检查,合格率为100%。100例NRDS患儿中,35例(35.00%)患儿MSCT示双肺野呈大片状,且形状不对称,边缘模糊,形成浸润影,双肺背部肺组织改变更明显;20例(20.00%)患儿MSCT示双肺纹理增多,且纹理模糊,沿肺纹理可见斑点状影或模糊小斑片状影;30例(30.00%)患儿MSCT示双肺呈低透亮度,可见磨玻璃样;15例(15.00%)患儿MSCT示双肺野密度均升高,且呈白肺,其中10例(10.00%)患儿呈现肺组织受压,纵隔移位至健侧表现。(2)100例NRDS患儿中,58例(58.00%)患儿X线示双肺可见广泛细颗粒网状影,心影、横隔均清晰可见,且伴有支气管充气征;42例(42.00%)患儿X线示双肺野一致性密度升高,见“白肺”及明显支气管充气征,心影、横隔边缘难以分辨;(3)以临床诊断为金标准,MSCT诊断NRDS的准确率为91.00%,敏感度为94.31%,特异性为66.67%;X线诊断N R D S的准确率为80.00%,敏感度为85.22%,特异性为41.67%;MSCT+X线对NRDS的诊断效能最高,诊断准确率为97.00%,敏感度为97.73%,特异性为91.67%。结论低剂量MSCT及X线对NRDS均具备良好的诊断价值,且MSCT相较X线诊断效能更高,但两者联合诊断可进一步提高诊断效能。展开更多
基金National Natural Science Foundation of China under grant (62071281)Local Science and Technology Development Fund Project Guided by the Central Government under grant (YDZJSX2021A003)。
文摘Radiation dose reduction in computed tomography(CT)can be achieved by decreasing the number of projections.However,reconstructing CT images via filtered back projection algorithm from sparse-view projections often contains severe streak artifacts,affecting clinical diagnosis.To address this issue,this paper proposes TransitNet,an iterative unrolling deep neural network that combines model-driven data consistency,a physical a prior constraint,with deep learning’s feature extraction capabilities.TransitNet employs a novel iterative architecture,implementing flexible physical constraints through learnable data consistency operations,utilizing Transformer’s self-attention mechanism to model long-range dependencies in image features,and introducing linear attention mechanisms to reduce self-attention’s computational complexity from quadratic to linear.Extensive experiments demonstrate that this method exhibits significant advantages in both reconstruction quality and computational efficiency,effectively suppressing streak artifacts while preserving structures and details of images.
基金supported by the National Natural Science Foundation of China(Nos.U2032148,U2032157,11775224)USTC Research Funds of the Double First-Class Initiative(No.YD2310002008)the National Key Research and Development Program of China(No.2017YFA0402904),the Youth Innovation Promotion Association,CAS(No.2020457)。
文摘Grating-based X-ray phase-contrast imaging enhances the contrast of imaged objects,particularly soft tissues.However,the radiation dose in computed tomography(CT)is generally excessive owing to the complex collection scheme.Sparse-view CT collection reduces the radiation dose,but with reduced resolution and reconstructed artifacts particularly in analytical reconstruction methods.Recently,deep learning has been employed in sparse-view CT reconstruction and achieved stateof-the-art results.Nevertheless,its low generalization performance and requirement for abundant training datasets have hindered the practical application of deep learning in phase-contrast CT.In this study,a CT model was used to generate a substantial number of simulated training datasets,thereby circumventing the need for experimental datasets.By training a network with simulated training datasets,the proposed method achieves high generalization performance in attenuationbased CT and phase-contrast CT,despite the lack of sufficient experimental datasets.In experiments utilizing only half of the CT data,our proposed method obtained an image quality comparable to that of the filtered back-projection algorithm with full-view projection.The proposed method simultaneously addresses two challenges in phase-contrast three-dimensional imaging,namely the lack of experimental datasets and the high exposure dose,through model-driven deep learning.This method significantly accelerates the practical application of phase-contrast CT.
文摘目的探究低剂量多层螺旋CT(MSCT)与X线在新生儿呼吸窘迫综合征(NRDS)诊断中的应用价值。方法收集2019年1月至2024年2月我院收治的100例NRDS的病例资料,均接受MSCT及X线检查,观察其MSCT及X线特点,评估其两者对NRDS的诊断效能。结果(1)100例NRDS患儿的MSCT图像中图像质量优42例(42.00%),图像质量良37例(37.00%),图像质量合格20例(20.00%),1例患儿图像质量不合格,图像质量合格率为99.00%,经镇静后重新检查,合格率为100%。100例NRDS患儿中,35例(35.00%)患儿MSCT示双肺野呈大片状,且形状不对称,边缘模糊,形成浸润影,双肺背部肺组织改变更明显;20例(20.00%)患儿MSCT示双肺纹理增多,且纹理模糊,沿肺纹理可见斑点状影或模糊小斑片状影;30例(30.00%)患儿MSCT示双肺呈低透亮度,可见磨玻璃样;15例(15.00%)患儿MSCT示双肺野密度均升高,且呈白肺,其中10例(10.00%)患儿呈现肺组织受压,纵隔移位至健侧表现。(2)100例NRDS患儿中,58例(58.00%)患儿X线示双肺可见广泛细颗粒网状影,心影、横隔均清晰可见,且伴有支气管充气征;42例(42.00%)患儿X线示双肺野一致性密度升高,见“白肺”及明显支气管充气征,心影、横隔边缘难以分辨;(3)以临床诊断为金标准,MSCT诊断NRDS的准确率为91.00%,敏感度为94.31%,特异性为66.67%;X线诊断N R D S的准确率为80.00%,敏感度为85.22%,特异性为41.67%;MSCT+X线对NRDS的诊断效能最高,诊断准确率为97.00%,敏感度为97.73%,特异性为91.67%。结论低剂量MSCT及X线对NRDS均具备良好的诊断价值,且MSCT相较X线诊断效能更高,但两者联合诊断可进一步提高诊断效能。
