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- Title
基于神经网络的超声背散射零差 K 成像脂肪肝 评价方法研究.
- Authors
丁琪瑛; 吴水才; 崔博翔; 周著黄
- Abstract
To propose two neural network estimator-based methods respectively for estimating ultrasound backs- catter homodyned-K model parameters and evaluating hepatic steatosis with ultrasound backscatter homodyned-K imaging to solve the problem of high complexity of the traditional estimation method of ultrasound backscatter homodyned-K model parameters. Firstly, simulated ultrasonic backscatter envelope signal samples were obtained using Monte Carlo simulation, and then the feature parameters were extracted and input to the trained neural network model to gain the estimation results of homodyned -K model parameters. Secondly, the homodyned -K model parameters for the backscatter envelope signals within the windows were estimated by the sliding window method, and the matrix of the estimated homodyned-K model parameters underwent scaning transformation, color mapping and setting of region of interest, and the parameter images in the region of interest were superimposed onto the B-ultrasound images to realize ultrasound backscatter homodyned -K imaging. Finally, the estimation accuracy of the neural network -based parameter estimation method for ultrasound backscatter homodyned -K model was verified by computer simulation experiments, and the performance of the neural network -based ultrasound backscatter homodyned -K imaging for hepatitis steatosis assessment was validated by clinical trials. The results of computer simulation experiments showed that the relative root-mean-square errors of estimating ultrasound backscatter homodyned -K model parameters lg α and k by the neural network -based method were 0.505 and 0.408, respectively, and the estimation accuracy was enhanced significantly when compared with the traditional estimation method. The results of clinical trials indicated that the AUC values of the neural network -based ultrasound backscatter homodyned-K model parameters αNN and kNN for detecting steatosis ≥S1, ≥S2, and ≥S3 were 0.77, 0.84, 0.87 and 0.77, 0.84, 0.84, respectively, and the performance of hepatic steatosis assessment was increased greatly when compared with the traditional ultrasound backscatter homodyned-K imaging. The proposed neural network-based method with ultrasound backscatter homodyned -K imaging provides a new means for quantitative ultrasound evaluation of hepatic steatosis.
- Publication
Chinese Medical Equipment Journal, 2023, Vol 44, Issue 1, p19
- ISSN
1003-8868
- Publication type
Article
- DOI
10.19745/j.1003-8868.2023004