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(
作者
向青
曹键
罗涛
朱璇
覃杰
郭亚豪
黎超
XIANG Qing;CAO Jian;LUO Tao;ZHU Xuan;QIN Jie;GUO Yahao;LI Chao(Department of Radiology,the Third Affiliated Hospital of Sun Yat-sen University,Guangzhou 510630,China)
出处
《新医学》
CAS
2024年第9期685-692,共8页
Journal of New Medicine
基金
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)。
关键词
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 reconstruction
Adaptive statistical iterative reconstruction
Coronary CT angiography
Signal-to-noise ratio
Contrast-to-noise ratio
