Objective To investigate image quality and clinical value of dual-source dual energy virtual non-contrast (VNC) CT of the head. MethodsSixty-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) HU]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) mSy](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 (χ2 = 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 (χ2 =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 (χ2 = 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.