深度学习图像重建实现胸部能谱CT虚拟平扫对真实平扫的临床替代OA
Deep learning image reconstruction enables virtual non-contrast to replace true non-contrast in chest spectral CT
目的 探索深度学习图像重建(DLIR)在优化胸部能谱CT虚拟平扫(VNC)图像质量的应用价值.方法 前瞻性收集2024年6~10月于陕西中医药大学附属医院行胸部能谱CT真实平扫(TNC)及双期增强扫描的45例患者.采用ASIR-V50%权重重建120 kVp-like图像作为真实平扫对照图像(TNC-AR50),基于动脉期和静脉期增强数据,分别采用中、高等级DLIR(DLIR-M和DLIR-H)重建4组VNC图像(VP-VNC-DM、VP-VNC-DH、AP-VNC-DM、AP-VNC-DH).在5组图像(TNC-AR50+4组VNC)上测量主动脉、皮下脂肪、竖脊肌及病灶的CT值、噪声(SD),并计算信噪比(SNR)和对比噪声比(CNR).采用单因素方差分析和Kruskal-Wallis检验比较客观指标.2位放射科医师独立采用5分制Likert量表对整体图像质量及病灶可见性进行主观盲法评价.结果 在客观图像质量评价上,VP-VNC-DH组图像质量优于TNC-AR50,且5组图像间的CT值差异无统计学意义(P>0.05);VP-VNC-DH组的图像噪声最低,SNR、CNR最高.在主观评价方面,VP-VNC-DH组的图像质量评分最高且在病灶显示度方面表现最佳.胸部CT增强扫描时,有、无TNC扫描的总有效辐射剂量分别为9.40±0.41 mSv和6.27±0.28 mSv.无TNC扫描,总辐射剂量减少约33.3%.结论 在胸部增强CT检查中,基于DLIR(尤其是静脉期DLIR-H)重建的VNC图像质量显著优于基于ASIR-V 50%重建的TNC,且CT值一致性良好.推荐采用静脉期DLIR-H重建VNC图像替代真实平扫,以有效降低辐射剂量.
Objective To explore the application value of deep learning image reconstruction(DLIR)in optimizing the image quality of virtual non-contrast(VNC)chest spectral CT.Methods Forty-five patients undergoing true non-contrast(TNC)and dual-phase contrast-enhanced spectral CT of the chest at the Affiliated Hospital of Shaanxi University of Chinese Medicine from June to October 2024 were prospectively enrolled.ASIR-V50%weighted reconstruction at 120 kVp-like settings served as the true non-contrast reference(TNC-AR50).Based on arterial and venous phase contrast data,four DLIR-reconstructed VNC groups(VP-VNC-DM,VP-VNC-DH,AP-VNC-DM,AP-VNC-DH).CT values,noise(SD),SNR,and CNR were measured for the aorta,subcutaneous fat,erector spinae muscles,and lesions across all five image sets(TNC-AR50+4 VNC sets).Objective metrics were compared using one-way ANOVA and Kruskal-Wallis tests.Two radiologists independently performed subjective blinded evaluations of overall image quality and lesion visibility using a 5-point Likert scale.Results In objective image quality assessment,the VP-VNC-DH group demonstrated superior quality compared to TNC-AR50,with no statistically significant differences in CT values among the five groups(P>0.05).The VP-VNC-DH group exhibited the lowest image noise and the highest SNR and CNR.In subjective evaluation,the VP-VNC-DH group received the highest image quality scores and performed best in lesion conspicuity.The total effective radiation dose for chest CT with and without the TNC scan was 9.40±0.41 mSv and 6.27±0.28 mSv,respectively.Omitting the TNC scan reduced the total radiation dose by approximately 33.3%.Conclusion In chest-enhanced CT examinations,VNC images reconstructed using DLIR(especially venous-phase DLIR-H)demonstrated significantly superior image quality compared to TNC images reconstructed using ASIR-V 50%,with good CT value consistency.It is recommended to use venous-phase high-level DLIR(DLIR-H)reconstruction for VNC images as an alternative to true non-contrast scans to effectively reduce radiation dose.
徐龙;李鑫;党珊;于楠;贾永军;段海峰
西电集团医院医学影像科,陕西 西安 710077陕西中医药大学医学技术学院,陕西 咸阳 712046陕西中医药大学附属医院医学影像科,陕西 咸阳 712000陕西中医药大学医学技术学院,陕西 咸阳 712046陕西中医药大学附属医院医学影像科,陕西 咸阳 712000西电集团医院医学影像科,陕西 西安 710077
深度学习重建算法虚拟平扫胸部CT辐射剂量
deep learning reconstruction algorithmvirtual plain scanchest CTradiation dose
《分子影像学杂志》 2026 (1)
44-49,6
陕西省教育厅青年创新团队科学研究计划项目(24JP049)
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