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Hemorrhagic transformation (HT) is one of the common complications in patients with acute ischemic stroke (AIS). This study aims to investigate the value of different thresholds of Tmax generated from perfusion-weighted MR imaging (PWI) and the apparent diffusion coefficient (ADC) value in the prediction of HT in AIS. A total of 156 AIS patients were enrolled in this study, with 55 patients in the HT group and 101 patients in non-HT group. The clinical baseline data and multi-parametric MRI findings were compared between HT and non-HT groups to identify indicators related to HT. The optimal parameters for predicting HT and the corresponding cutoff values were obtained using the receiver operating characteristic curve analysis of the volumes of ADC < 620 × 10−6 mm2/s and Tmax > 6 s, 8 s, and 10 s. The results showed that the volumes of ADC < 620 × 10−6 mm2/s and Tmax > 6 s, 8 s, and 10 s in the HT group were all significantly larger than that in the non-HT group and were all independent risk factors for HT. Early measurement of the volume of Tmax > 10 s had the highest value, with a cutoff lesion volume of 10.5 mL. [ABSTRACT FROM AUTHOR]

This study investigated the quantitative distribution of cerebral venous oxygen saturation (SvO2) based on quantitative sensitivity mapping (QSM) and determined its prognostic value in patients with acute ischemic stroke (AIS). A retrospective study was conducted on 39 hospitalized patients. Reconstructed QSM was used to calculate the cerebral SvO2 of each region of interest (ROI) in the ischemic hemisphere. The intraclass correlation coefficient (ICC) and Bland–Altman analysis were conducted to define the best resolution of the distribution map. The correlation between the cerebral SvO2 in hypoxic regions (SvO2ROI < 0.7) and clinical scores was obtained by Spearman and power analysis. The associations between cerebral SvO2 and unfavorable prognosis were analyzed using multivariate logistic regression. Excellent agreement was found between the cerebral SvO2 in hypoxic regions with a resolution of 7.18 × 7.18 × 1.6 mm3 and asymmetrically prominent cortical veins regions (ICC: 0.879 (admission), ICC: 0.906 (discharge)). The cerebral SvO2 was significantly negative with clinical scores (all |r| > 0.3). The cerebral SvO2 and its changes at discharge were significantly associated with an unfavorable prognosis (OR: 0.812 and 0.866). Therefore, the cerebral SvO2 in hypoxic regions measured by the quantitative distribution map can be used as an indicator for evaluating the early prognosis of AIS. [ABSTRACT FROM AUTHOR]

BACKGROUND: Acute ischemic stroke (AIS) results in high morbidity, disability, and mortality. Early and automatic diagnosis of AIS can help clinicians administer the appropriate interventions. OBJECTIVE: To develop a deep symmetric 3D convolutional neural network (DeepSym-3D-CNN) for automated AIS diagnosis via diffusion-weighted imaging (DWI) images. METHODS: This study includes 190 study subjects (97 AIS and 93 Non-AIS) by collecting both DWI and Apparent Diffusion Coefficient (ADC) images. 3D DWI brain images are split into left and right hemispheres and input into two paths. A map with 125×253×14×12 features is extracted by each path of Inception Modules. After the features computed from two paths are subtracted through L-2 normalization, four multi-scale convolution layers produce the final predation. Three comparative models using DWI images including MedicalNet with transfer learning, Simple DeepSym-3D-CNN (each 3D Inception Module is replaced by a simple 3D-CNN layer), and L-1 DeepSym-3D-CNN (L-2 normalization is replaced by L-1 normalization) are constructed. Moreover, using ADC images and the combination of DWI and ADC images as inputs, the performance of DeepSym-3D-CNN is also investigated. Performance levels of all three models are evaluated by 5-fold cross-validation and the values of area under ROC curve (AUC) are compared by DeLong's test. RESULTS: DeepSym-3D-CNN achieves an accuracy of 0.850 and an AUC of 0.864. DeLong's test of AUC values demonstrates that DeepSym-3D-CNN significantly outperforms other comparative models (p < 0.05). The highlighted regions in the feature maps of DeepSym-3D-CNN spatially match with AIS lesions. Meanwhile, DeepSym-3D-CNN using DWI images presents the significant higher AUC than that either using ADC images or using DWI-ADC images based on DeLong's test (p < 0.05). CONCLUSIONS: DeepSym-3D-CNN is a potential method for automatically identifying AIS via DWI images and can be extended to other diseases with asymmetric lesions. [ABSTRACT FROM AUTHOR]