Non-contrast enhanced computed tomography (NCCT) is usually performed to estimate bleeding complications immediately after procedures. However, hyperdense areas on NCCT have not yet been understood; different interpretations have been reported in the literature. It remains unclear whether NCCT performed immediately after intra-arterial revascularization (IAR) could be useful for predicting hemorrhagic transformation (HT) or clinical outcomes. Therefore, we investigated the diagnostic values of hyperdense areas on NCCT images obtained immediately after IAR. This was a retrospective study of acute ischemic stroke patients who underwent IAR between October 2007 and December 2010. NCCT scans were routinely obtained immediately after IAR and additional follow-up imaging protocols included diffusion weighted imaging (DWI)/gradient echo imaging (GRE) 24 h after IAR. HT was assessed by means of GRE obtained 24 h after IAR. Hounsfield Unit (HU) of the hyperdensity was measured in the manually drawn regions of interest. A total of 68 patients were analyzed in this study. Twenty-nine patients (42.6%) developed HT on follow-up images. Thirty-eight patients had hyperdense areas on NCCT immediately after IAR. Hyperdensity on NCCT performed immediately after IAR revealed 23 (60.5%) of the 38 patients with six false negative areas. NCCT performed immediately after IAR showed a sensitivity of 79.3%, a specificity of 61.5%, a positive predictive value of 60.5% and a negative predictive value of 80% for HT. The HU value was a predictor of HT without statistical significance (area under curve of 0.629; 95% CI: 0.49-0.76; p = 0.068). In addition, an HU of >90 poorly predicted HT with a low sensitivity (23%) and a high specificity (94%). In conclusion, our results showed that although hyperdensity on NCCT images obtained immediately after IAR had a moderate predictive value for HT, there were limitations to the prediction of subsequent parenchymal hematoma and symptomatic intracranial hemorrhage, with a low specificity and a low positive predictive value.