双源CT虚拟平扫在头部检查的初步应用

Dual-source virtual non-contrast CT of the head： a preliminary study

目的探讨双源CT双能量头部虚拟平扫（VNC）的图像质量和临床应用价值。方法对62例临床怀疑脑血管病变的患者，使用双源CT进行头部常规平扫（CNC）和双能量CTA检查，利用双能量软件得到VNC图像。比较CNC和VNC图像灰质、白质、脑脊液、高密度出血性和低密度缺血性病变的CT值，使用4分法对图像质量进行主观评价，比较两组图像的噪声、辐射剂量和病变检出率，使用配对t检验、Wilcoxon符号秩检验和X2检验（McNemar检验和Kappa检验）进行统计分析。结果CNC与VNC图像灰质、白质、脑脊液、高密度病变及低密度病变的CT值分别为[（43．3±1．5）和（33．2±1．3）HU，t=46．98]、[（32．9±1．3）和（28．8±1．6）HU，t=16．28I、（9．0±1．4）和（5．3±1．9）HU，t=12．41]、[（62．8±10．0）和（51．3±11．5）HU，z=-4．37]、[（20．7±4．7）和（18．0±6．9）HU，t=3．84]，差异均有统计学意义（P值均〈0．01）。VNC图像噪声[（1．63±0．34）HU]大于CNC图像[（0．99±0．18）HU]（Z=-6．41，P〈0．01）。VNC图像有效剂量[（0．53±0．08）rosy]低于CNC[（1．37±0．23）rosy]（Z=-6．45，P〈0．01）。CNC和VNC图像噪声、颅底伪影、灰白质对比、高密度和低密度病变显示的主观评分分别为（3．9±0．3）和（2．7±0．5）分、（3．7±0．5）和（2．4±0．9）分、（3．3±0．6）和（1．3±0．5）分、（4．0±0．0）和（3．0±0．4）分、（3．9±0．3）和（3．2±0．8）分，VNC图像噪声与颅底伪影的评分较CNC图像低（Z值分别为-6．84、-6．15，P值均〈0．01），灰白质对比、高密度和低密度病变显示低于CNC图像（Z值分别为-7．01、-4．52和-3．12，P值均〈0.01）。在个体水平，VNC图像显示高密度出血性病变29例，无假阳性和假阴性病例，敏感性、特异性、阳性预测值和阴性预测值均为100．0％（29／29、33／33、29／29、33／33），与CNC一致（P〉0．05，Kappa值=1．000）；VNC图像显示低密度缺血性病变22例，假阳性1例，假阴性2例，敏感性、特异性、阳性预测值和阴性预测值分别为91．3％（21／23）、97．4％（38／39）、95．5％（21／22）和95．0％（38／40），与CNC图像（23例）差异无统计学意义（X2=0．00，P〉0．05，Kappa值=0．895）。在病灶水平，VNC图像显示出血灶53个，假阴性4个，无假阳性，敏感性、特异性、阳性预测值和阴性预测值分别为93．0％（53／57）、100．0％（38／38）、100．0％（53／53）和90．5％（38／42），VNC图像对出血灶的显示率与CNC差异无统计学意义（X2=2．25，P〉0．05，Kappa值=0．914）；VNC图像显示低密度病灶38个，假阳性2个，假阴性13个，敏感性、特异性、阳性预测值和阴性预测值分别为73．5％（36／49）、96．4％（53／55）、94．7％（36／38）和80．3％（53／66），VNC图像对低密度病灶的显示率低于CNC（X2=6．67，P〈0．01，Kappa值=0．707）。结论与CNC相比，头部VNC辐射剂量低，但图像质量下降，对出血性病变具有替代CNC的潜在使用价值，对缺血性病变也有一定的参考价值。

Objective To investigate image quality and clinical value of dual-source dual energy virtual non-contrast （ VNC ） CT of the head. Methods Sixty-two patients suspected of cerebrovascular diseases underwent conventional non-contrast （CNC） CT and dual energy CTA examination of the head with dual-source CT. Virtual non-contrast images were reconstructed using dual energy software. The CT values of gray matter, white matter, cerebrospinal fluid, hyperdense hemorrhagic lesion and hypodense ischemic lesion were compared between CNC and VNC images. A four-score scale was used to assess image quality subjectively. Image noise, radiation dosage and detection rate were compared between CNC and VNC images. Paired t test, Wilcoxon signed ranks test and Chi-square test （McNemar test and Kappa test） were used. Results The CT value on CNC and VNC images, were （43.3 ± 1.5） and （33.2 ± 1.3 ） HU for gray matter （t=46.98, P〈0.01）, （32.9 ±1.3） and （28.8 ±1.6） HU for white matter（t=16.28, P〈 0.01）, （9.0 ± 1.4） and （5.3 ± 1.9） HU for cerebrospinal fluid （t =12.41, P〈0.01）,（62.8 ± 10.0） and （51.3 ± 11.5） HU for hyperdense lesion （Z = -4.37, P 〈 0.01）, （20.7 ±4.7） and （18.0 ±6.9） HU for hypodense lesion （ t = 3.84, P 〈 0. 01 ）, respectively. VNC images [ （ 1.63 ± 0.34） HUI had more noise than CNC images [（0.99 ±0.18） HU] （Z= -6.41, P〈0.01）. VNC [ （0. 53 ± 0. 08） mSv] had less effective dose than CNC [ （1.37 ± 0. 23） mSv] （Z = -6. 45, P 〈 0. 01 ）. In subjective assessment, VNC images had more noise （ 2. 7 ± 0. 5 for VNC and 3.9 ± 0. 3 for CNC, Z = -6. 84, P 〈 0. 01 ） and skull base-related artifacts （2.4 ± 0. 9 for VNC and 3.7 ± 0. 5 for CNC, Z = -6. 15, P 〈 0. 01 ） than CNC images. The gray/white matter contrast （1.3 ± 0. 5 for VNC and 3.3 ± 0. 6 for CNC, Z = - 7.01, P 〈 0. 01 ）, hyperdense lesion display （3.0 ± 0.4 for VNC and 4. 0 ± 0. 0 for CNC,Z = - 4. 52, P 〈 0. 01 ） and hypodense lesion display （3.2 ± 0. 8 for VNC and 3.9 ± 0. 3 for CNC, Z = - 3.12, P 〈 0. 01 ） on VNC images were lower than those on CNC images. In per-patient analysis, 29 cases of hyperdense lesion （ hemorrhage ） were found on VNC images without misdiagnosis. The sensitivity, specificity, positive predictive value and negative predictive value were all 100. 0% （29/29, 33/33, 29/29, 33/33）. VNC images had the same detection rate of hyperdense lesions as CNC images （P 〉 0.05, Kappa = 1. 000 ） at per-patient level. Twenty-two patients with hypodense ischemic lesions were found on VNC images with one false positive case and two false negative cases. The sensitivity, specificity, positive predictive value and negative predictive value were 91.3% （21/23）, 97.4% （38/39）, 95.5% （21/22） and 95.0% （38/40） respectively. No statistical difference was found in detecting hypodense lesions between VNC and CNC images （ X2 = 0. 00, P 〉 0. 05, Kappa = 0. 895）. In per-lesion analysis, 53 hemorrhage lesions were found on VNC images with false negative results of four lesions and no false positive result. The sensitivity, specificity, positive predictive value and negative predictive value were 93.0% （53/57）, 100. 0% （38/38）, 100. 0% （53/53） and 90. 5% （38/42） respectively. There was no significant difference in detection rate of hyperdense lesion between VNC and CNC images （Xz =2. 25, P 〉0. 05, Kappa =0. 914）. Thirty-eight hypodense lesions were found on VNC images with 2 false positive lesions and 13 false negative lesions. The sensitivity, specificity, positive predictive value and negative predictive value were 73.5% （ 36/49 ）, 96.4% （ 53/55 ）, 94. 7% （ 36/38 ） and 80. 3% （53/66） respectively. The detection rate of hypodense lesion on VNC images was lower than that on CNC images （ X2 = 6. 67,P 〈 0. 01, Kappa = 0. 707 ）. Conclusion Compared with CNC images, head VNC images have reduced image quality and radiation dosage. VNC images can replace CNC images potentially in detecting intracranial hemorrhage and provide information for ischemic cerebrovascular diseases to some extent.