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In many medical image applications, high-resolution images are needed to facilitate early diagnosis. However, due to technical limitations, it may not be easy to obtain an image with ideal resolution especially for the diffusion weighted imaging (DWI). Super-resolution (SR) technology is developed to solve this problem by generating high-resolution (HR) images from low-resolution (LR) images. The purpose of this study is to obtain the SR-DWI from the original LR image through deep super-resolution network. The effectiveness of the SR image is assessed by radiomic analysis in predicting the histological grade of breast cancer. To this end, a dataset of 144 breast cancer cases were collected, including 83 cases who diagnosed as high-grade malignant (Grade 3) breast cancer, and 61 who were median-grade malignant (Grade 2). For each case, the dynamic enhanced magnetic resonance imaging (DCE-MRI), and the apparent diffusion coefficients (ADC) map derived from DWI were obtained. Lesion segmentation was performed on each of the original ADC and the SR-ADC, in which 30 texture and 10 statistical features were extracted. Deep SR model was established by an end-to-end training from the LR DCE-MRI and the HR counterparts and was applied to the ADC images to obtain SR-ADCs. Univariate and multivariate logistic regression classifier was implemented to evaluate the performance of the individual feature and collective features, respectively. The model performance was evaluated by the area under the curve (AUC) under leave one-out cross-validation (LOOCV). For the individual feature analysis, the performance in terms of AUC was significantly better based on the SR-ADC image than that based on the original ADC image. For multivariate analysis, the classifier performance in terms of AUCs were 0.848±0.061 and 0.878±0.051 for the original ADC and the SR ADC, respectively. The results suggested that the enhanced resolution of ADC image had the potential to more accurately predict histological grade in breast cancer.
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