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
目的研究冠状动脉CT血管成像(CCTA)评价冠状动脉慢性完全闭塞病变(chronic total occlusion,CTO)形态学参数在介入治疗指导中的应用价值。方法选取2021年1月至2023年12月金华市人民医院收治的经冠状动脉造影(ICA)证实的CTO患者300例,患...目的研究冠状动脉CT血管成像(CCTA)评价冠状动脉慢性完全闭塞病变(chronic total occlusion,CTO)形态学参数在介入治疗指导中的应用价值。方法选取2021年1月至2023年12月金华市人民医院收治的经冠状动脉造影(ICA)证实的CTO患者300例,患者术前均接受CCTA检查。记录CCTA形态学参数闭塞段近端形态、闭塞血管长度、闭塞段内线样强化长度、闭塞段内线样强化长度/闭塞血管长度、闭塞段血管线样强化、闭塞段内血管钙化情况、闭塞段内血管钙化面积≥横截面50%、病变走行迂曲(>45°)、侧支血管情况、血管开口病变,并分析以上参数与PCI治疗结果的关系。结果300例CTO患者病变共325处,PCI治疗成功227处(69.85%),PCI治疗失败98处(30.15%);失败组闭塞段近端钝形、闭塞血管长度、闭塞段内血管钙化面积≥横截面50%、病变走行迂曲(>45°)明显高于成功组(P<0.05),闭塞段内线样强化长度、闭塞段内线样强化长度/闭塞血管长度、闭塞段内线样强化明显低于成功组(P<0.05),两组其余参数差异均无统计学意义(P>0.05);多因素logistic回归分析结果显示,闭塞段内线样强化长度(OR=1.975,95%CI:1.306~2.988)、闭塞段内线样强化长度/闭塞血管长度(OR=3.831,95%CI:1.332~11.017)、闭塞段内线样强化(OR=1.702,95%CI:1.007~2.879)是预测PCI治疗成功的相关因素(P<0.05)。结论CCTA评价冠状动脉CTO形态学参数在介入治疗中具有一定的指导作用,其中闭塞段内线样强化长度、闭塞段内线样强化长度/闭塞血管长度、闭塞段内线样强化是预测PCI治疗成功的相关因素。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.
文摘目的研究冠状动脉CT血管成像(CCTA)评价冠状动脉慢性完全闭塞病变(chronic total occlusion,CTO)形态学参数在介入治疗指导中的应用价值。方法选取2021年1月至2023年12月金华市人民医院收治的经冠状动脉造影(ICA)证实的CTO患者300例,患者术前均接受CCTA检查。记录CCTA形态学参数闭塞段近端形态、闭塞血管长度、闭塞段内线样强化长度、闭塞段内线样强化长度/闭塞血管长度、闭塞段血管线样强化、闭塞段内血管钙化情况、闭塞段内血管钙化面积≥横截面50%、病变走行迂曲(>45°)、侧支血管情况、血管开口病变,并分析以上参数与PCI治疗结果的关系。结果300例CTO患者病变共325处,PCI治疗成功227处(69.85%),PCI治疗失败98处(30.15%);失败组闭塞段近端钝形、闭塞血管长度、闭塞段内血管钙化面积≥横截面50%、病变走行迂曲(>45°)明显高于成功组(P<0.05),闭塞段内线样强化长度、闭塞段内线样强化长度/闭塞血管长度、闭塞段内线样强化明显低于成功组(P<0.05),两组其余参数差异均无统计学意义(P>0.05);多因素logistic回归分析结果显示,闭塞段内线样强化长度(OR=1.975,95%CI:1.306~2.988)、闭塞段内线样强化长度/闭塞血管长度(OR=3.831,95%CI:1.332~11.017)、闭塞段内线样强化(OR=1.702,95%CI:1.007~2.879)是预测PCI治疗成功的相关因素(P<0.05)。结论CCTA评价冠状动脉CTO形态学参数在介入治疗中具有一定的指导作用,其中闭塞段内线样强化长度、闭塞段内线样强化长度/闭塞血管长度、闭塞段内线样强化是预测PCI治疗成功的相关因素。
