Compensating for Size Effect in Shear Wave Elastography Using Deep Neural Networks

Abstract

In ultrasound shear wave elastography (USWE), the elasticity of a small lesion is underestimated due to the wave reflection inside the lesion. This paper proposes using a deep neural network to compensate for the size effect without explicit size information. The deep neural network corrects the underestimation of the elastic modulus. The dataset for the training process consists of 4000 images with lesions of random sizes and elastic moduli, obtained from the k-Wave MATLAB Toolbox. A mean absolute error (MAE) is calculated between the ground truth and the image generated by the generator network for 500 test datasets with the unitary elasticity background. The maximum value of MAE is 0.0082, indicating that the generator network generates images that are similar to the ground truth in size and modulus. When the unitary elasticity background is replaced by the image of the breast phantom, the neural network proves to be effective in size effect compensation. © 2023 ICROS.