Prediction of Anemia From Cerebral Venous Sinus Attenuation on Deep-Learning Reconstructed Brain Computed Tomography Images

Abstract

The aim of the study is to evaluate whether the prediction of anemia is possible using quantitative analyses of unenhanced cranial computed tomography (CT) with deep learning reconstruction (DLR) compared with conventional methods. Methods:This cross-sectional retrospective study included 116 participants (76 males; mean age, 66.7) who had hemoglobin (Hb) levels obtained within 24 hours of unenhanced cranial CT, which included 2 reconstruction methods: DLR and hybrid iterative reconstruction. Regions of interest were the confluence of sinuses (CoS) and the right and left transverse sinuses. In addition, edge rise distance of cerebrospinal fluid and venous was measured.Results: Spearman rank correlation coefficient demonstrated a positive association between Hb levels and sinus attenuation values. Among these, the CoS in DLR had the best correlation (r = 0.703, P < 0.001). For the prediction of anemia (Hb < 11 g/dL), the area under the curve of CoS in DLR (area under the curve = 0.874; 95% confidence interval, 0.798-0.949; P < 0.001) was the highest; however, there were no significant differences among reconstruction method and sinus. The attenuation values of DLR were significantly higher than those of hybrid iterative reconstruction ( P < 0.001, paired t test), and the differences between the 2 methods were 4.1 (standard deviation [SD], 1.6) for CoS, 5.2 (SD, 2.2) for right transverse sinuses, and 5.8 (SD, 2.4) for left transverse sinuses. The signal-to-noise ratio ( P < 0.001, paired t test) and edge rise distance ( P < 0.001, Wilcoxon signed rank test) of DLR was significantly higher. Conclusions:Higher CT attenuation values should be considered for predicting anemia based on brain DLR images.

Deep learning reconstruction for improving the visualization of acute brain infarct on computed tomography

“…However, there also exist reports which reported the difference of CT attenuation of the liver between model-based iterative reconstruction and filtered back projection [ 23 , 24 ]. In addition, Yamakuni, et al recently reported that the CT attenuation of the cerebral venous sinus in DLR was significantly higher than that in Hybrid IR [ 25 ]; our results would be in line with their article.…”

Deep learning reconstruction for high-resolution computed tomography images of the temporal bone: comparison with hybrid iterative reconstruction

“…In a systematic review of abdominal CT with DLR, CT attenuation was reported to be similar with DLR and HIR [ 19 ]. However, according to Yamakuni et al, CT attenuation of the cerebral sinus was observed to be significantly different with DLR and HIR [ 21 ]. Our study results accord with the findings in the latter report.…”

“…In supervised training of DLR, low-quality data and high-quality images are respectively used as the input and reference data. A trained DLR is known to reduce image noise [ 19 ], thereby improving the quality of CT images in regions as varied as the head [ 20 , 21 ], chest [ 9 ], abdomen [ 10 , 19 , 22 , 23 ], and vertebrae [ 11 , 24 , 25 ]. We therefore hypothesized that DLR can also improve the quality of high-resolution CT of the temporal bone.…”

Deep-Learning Reconstruction of High-Resolution CT Improves Interobserver Agreement for the Evaluation of Pulmonary Fibrosis

“…A higher interobserver agreement in the evaluation of ILD for DLR would have been resulted from an improved image quality. A better image noise reduction can be achieved compared to HIR in various regions such as the head, 19,27 vertebra, 21,23,24 and abdomen 20,22 as well as the chest. 25 Our study result corroborates with other studies, which revealed a significantly reduced quantitative image noise in DLR compared to HIR.…”