Preliminary investigation on clutter filtering based on deep learning

Abstract

In recent years, singular value decomposition (SVD)-based clutter filters have received widespread attention in ultrasound flow imaging owing to their high performance over traditional clutter filters in suppressing clutter signals. The excellent performance of the SVD clutter filter depends on its adaptive nature. The SVD clutter filter adaptively rejects echoes from slowly moving clutters, allowing visualization of echoes from blood cells. Owing to this property, the SVD filter works well throughout a cardiac cycle. Recently, deep neural networks have been used for a variety of tasks. The adaptive nature of deep neural networks would be beneficial for clutter filtering in ultrasonic blood flow imaging. In the present study, we conducted a preliminary study on clutter filtering using a long short-term memory neural network. Experimental results suggested that the proposed deep-learning clutter filter achieved a comparable performance than SVD one in terms of contrast values.