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深度学习图像重建算法在80kV管电压下冠状动脉CT血管造影中的应用OACSTPCD

Application of CCTA under 80 kV tube voltage based on deep learning image reconstruction algorithm

中文摘要英文摘要

目的 探讨 80 kV深度学习图像重建(DLIR)算法在冠状动脉CT血管造影(CCTA)中的应用价值.方法 将接受心脏CCTA检查的 60 例患者按扫描方案分为 100 kV组(A组,n=30)和 80 kV组(B组,n=30).A组采用 60%权重自适应统计迭代重建-Veo(ASIR-V)算法(A-AV60)、DLIR算法(A-DLIR);B组采用DLIR算法(B-DLIR).记录 2 组的CT容积剂量指数(CTDIvol)、剂量长度乘积(DLP),计算有效辐射剂量(ED).将感兴趣区(ROI)分别置于主动脉根(AR)、左前降支(LAD)、左回旋支(LCX)、右冠状动脉(RCA)及同层胸前脂肪区域,记录各ROI的CT值、噪声值,计算信噪比(SNR)和对比噪声比(CNR).主观评价 2 组经 2 代冻结技术后的原始轴位、曲面重建(CPR)、容积再现(VR)重建和最大强度投影(MIP)重建,并且对 2 组图像进行主观质量评价.结果 B组较A组ED降低 45.14%.B-DLIR中AR、LAD、LCX、RCA的CT值均高于A-AV60 及A-DLIR,比较差异均有统计学意义(P均<0.001).A-DLIR与B-DLIR相比,AR、LAD、LCX的噪声值相近,仅在RCA中比较差异有统计学意义(P<0.05);A-DLIR与B-DLIR的噪声值均小于A-AV60,比较差异均有统计学意义(P均<0.001).A-DLIR与B-DLIR中AR、LAD、LCX、RCA的SNR、CNR相近,均高于A-AV60(P均<0.05).B-DLIR主观图像质量平均分高于A-AV60(P<0.05),但低于A-DLIR(P<0.05).A-DLIR与B-DLIR的清晰度、伪影、小分支可见度比较差异均无统计学意义(P均>0.05).结论 在CCTA检查中,采用 80 kV DLIR算法有助于获得质量更优的图像,进一步提高诊断效能,且可减少有效辐射剂量.

Objective To explore the application value of 80 kV deep learning image reconstruction(DLIR)algorithm in coronary CT angiography(CCTA).Methods Sixty patients who underwent CCTA were divided into two groups based on the scanning protocols:100 kV group(Group A,n=30)and 80 kV group(Group B,n=30).In Group A,60%ASIR-V(A-AV60)and DLIR high-level reconstruction(A-DLIR)was adopted.In Group B,DLIR high-level reconstruction(B-DLIR)was employed.The CT volumetric dose index(CTDIvol)and the dose length product(DLP)were recorded in both groups,and the effective dose(ED)was calculated.Regions of interest(ROI)were placed in the aortic root(AR),left anterior descending coronary artery(LAD),left circumflex coronary artery(LCX),right coronary artery(RCA),and the same-layer pectoral fat area.The CT values and noise values of each ROI were recorded.Signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)were calculated.Subjective evaluation was performed on the original axis,curved planar reconstruction(CPR),volume rendering(VR),and maximum intensity projection(MIP)reconstructions after the second-generation freeze technology(Snapshot Freeze 2,SSF-2),and the images in two groups were subject to subjective image quality evaluation.Results The ED in Group B was reduced by 45.14%compared to that in Group A.The CT values for AR,LAD,LCX,and RCA in the B-DLIR were higher than those in the A-AV60 and A-DLIR groups,and the differences were statistically significant(all P<0.001).The noise values for AR,LAD and LCX were similar,whereas statistical significance was observed in RCA between the A-DLIR and B-DLIR groups(P<0.05).The noise values in the A-DLIR and B-DLIR groups were smaller than that in the A-AV60 group,and the differences were statistically significant(both P<0.001).The SNR and CNR for AR,LAD,LCX and RCA were similar between the A-DLIR and B-DLIR groups,which were higher than those in the A-AV60 group(all P<0.05).The average subjective evaluation score of image quality in the B-DLIR group was higher than that in the A-AV60 group(P<0.05),whereas lower than that in the A-DLIR group(P<0.05).There were no significant differences in clarity,artifact and small branch visibility between the A-DLIR and B-DLIR groups(all P>0.05).Conclusions During CCTA,the 80 kV DLIR algorithm contributes to yielding high-quality images,further improves the diagnostic efficiency and reduces the irradiation dose.

向青;曹键;罗涛;朱璇;覃杰;郭亚豪;黎超

中山大学附属第三医院放射科,广东 广州 510630

深度学习图像重建自适应统计迭代重建冠状动脉CT血管造影信噪比对比噪声比

Deep learning image reconstructionAdaptive statistical iterative reconstructionCoronary CT angiographySignal-to-noise ratioContrast-to-noise ratio

《新医学》 2024 (009)

685-692 / 8

国家自然科学基金(822021291001447);中山大学附属第三医院"五个五"工程项目(2023ww605)

10.3969/j.issn.0253-9802.2024.09.002

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