Abstract:

Precise detection of hepatocellular carcinoma (HCC) is crucial for early cancer screening in medical ultrasound. Attenuation coefficient (AC) is emerging as a new biomarker for classifying tumors since it is sensitive to pathological changes in tissues. In this paper, a learning-based method to reconstruct AC image of abdominal regions from pulse-echo data obtained with a single ultrasound convex probe is presented. In the proposed method, the propagation delay caused by the variation of the sound speed of the medium is considered in the training phase to increase the reconstruction accuracy of the distant targets. In addition, the proposed network adaptively compensates the feature map according to the location of target area for accuracy. The proposed network was evaluated through simulation, and in-vivo tests. In simulation tests, the proposed network showed 3.8dB and 6% improvement over the baseline methods in PSNR and SSIM, respectively. In the in-vivo test, the proposed method classifies cysts, benign tumors and malignant tumors in the abdomen with a p-value of less than 0.02. The accuracy and robustness demonstrated by the proposed method show the broad clinical applicability of quantitative imaging in abdominal ultrasound.

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