深度学习重建算法对胸部低剂量CT肺结节测量及显示影响的模体研究  

The effect of chest low-dose CT combined with deep learning reconstruction algorithm on the display and measurement of pulmonary nodules:a phantom research

作　　者：刘海法 杨丽[1] 王琦[1] 杜煜[1] 赵茜茜 杨帆[1] 时高峰[1] LIU Hai-fa;YANG Li;WANG Qi(Department of CT and MRI,the Fourth Affiliated Hospital of Hebei Medical University,Shijiazhuang 050011,Hebei,China)

机构地区：[1]河北医科大学第四医院CT/磁共振科,河北石家庄050011

出　　处：《放射学实践》2023年第8期977-984,共8页Radiologic Practice

摘　　要：目的:基于胸部模体探讨不同强度深度学习重建算法(DLIR)对低剂量CT图像上肺结节显示及测量的影响。方法:采用包括纵隔、支气管血管束、软组织及骨骼的成年男性胸部仿真模型,内置直径(体积)为5 mm(66 mm^(3))、8 mm(268 mm^(3))和10 mm(523 mm^(3))的实性结节(SN)及磨玻璃结节(GGN),对其进行低剂量CT扫描(100 kVp、40 mA,CTDI VOI=0.84 mGy),采用标准卷积核的自适应统计迭代重建算法(ASIR-V)及中档(DLIR-M)和高档(DLIR-H)深度学习重建算法分别进行图像重建。在肺组织内放置ROI(面积100 mm^(2))测量肺组织CT值的标准差(SD)作为肺组织噪声(N肺组织)。选用肺结节CT影像辅助检测系统自动计算得到10 mm SN及10 mm GGN CT值的SD(即N结节)。计算3组图像上肺组织以及直径10 mm的SN和GGN的信噪比(SNR)及对比噪声比(CNR),以及所有结节的CT值和体积及其偏差度。对各组图像上肺组织及所有结节的噪声、支气管血管束的锐利度、SN和GGN的显示情况进行主观评价。结果:①在3组图像上,DLIR-H的肺组织、SN和GGN的噪声均为最低,肺组织的SNR、SN和GGN的SNR和CNR均为最高(P均<0.05),肺组织、SN和GGN显示情况的主观评分为最高(P均<0.001)。②三组图像上,三种直径SN的平均CT值偏差度的总体差异均无统计学意义(P均>0.05),三种直径GGN的平均CT值偏差度的总体差异均有统计学意义(P均<0.001);对于直径10 mm及5 mm的GGN,DLIR-M和DLIR-H图像上测得的平均CT值偏差度均小于ASIR-V(P均<0.001),DLIR-M和DLIR-H图像上测得的平均CT值偏差度的差异无统计学意义(P>0.05);对于直径8 mm的GGN,ASIR-V图像测得的平均CT值偏差度均小于DLIR-M、DLIR-H图像(P均<0.001),而DLIR-M与DLIR-H图像上测得的此指标值的差异无统计学意义(P=0.535)。③三组重建图像上测得10 mm、8 mm直径的SN及GGN体积偏差度的总体差异均无统计学意义(P均>0.05);对于直径5 mm的SN,ASIR-V与DLIR-M组间、DLIR-M与DLIR-HObjective:To evaluate the effect of chest low-dose CT combined with different strengths of TrueFidelity^(TM) deep-learning image reconstruction(DLIR)for the display and measurement of pulmonary nodules in the chest phantom.Methods:The adult male chest phantom(including mediastinum,bronchial vascular bundles,soft tissue and skeleton)implanted with solid and ground-glass nodules(SN and GGN)of 5mm(66mm^(3)),8mm(268mm^(3))and 10mm(523mm^(3))in diameter(volume)was scanned by CT with low dose(100kVp,40mA,CTDI_(VOI)=0.84mGy),and the raw data was reconstructed with Adaptive Statistical Iterative Reconstruction-Veo(ASIR-V)and the M-strength and H-strength of DLIR(DLIR-M and DLIR-H)at standard kernel for image reconstruction.A ROI(area of 100mm^(2))was placed in lung tissue to measure the standard deviation of lung tissue’s CT value,which was recorded as lung tissue noise(N_(Lung)).The CT values’standard deviation(SD)of 10mm SN and 10mm GGN was automatically calculated by pulmonary nodules CT image au-xiliary detection system,which was recorded as nodule noise(N_(Nod)).The signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)of lung tissue,10mm SN and GGN,as well as the mean CT values and volumes of all nodules and their deviations were calculated.The noise of lung tissue and all no-dules,the sharpness of bronchial vascular bundles,and the display of solid nodules and ground glass nodules in each group of images were subjectively assessed.Results:①The noise of lung tissue,SN and GGN in the DLIR-H images were all the lowest,and the SNR of lung tissue,the SNR and CNR of SN and GGN were all the highest among the three groups(all P<0.05),and the noise scores of lung tissue,SN and GGN were all the highest(all P<0.001).②There was no significant overall difference in the mean CT value’s deviation of all SN measured in the three groups of reconstructed images(all P>0.05);and the mean CT value’s deviation of all GGN in the three groups of images was statistically significant(all P<0.001):the mean CT value’s devia

关 键 词：肺结节 深度学习 图像重建 体层摄影术 X线计算机 图像质量 模体研究 

分 类 号：R814.42[医药卫生—影像医学与核医学] R734.2[医药卫生—放射医学]