Deep-learning-based skull-induced artifact reduction for transcranial ultrasound imaging: simulation study

Abstract

In the domain of brain imaging of small animals including rats, ultrasound (US) imaging is an appealing tool because it offers a high frame rate, easy access, and involves no radiation. However, the rat skull causes artifacts that influence brain image quality in terms of contrast and resolution. Therefore, minimizing the skull-induced artifacts in US imaging is a significant challenge. Unfortunately, the amount of literature on rat skull-induced artifacts is limited, and there is a particular lack of studies exploring reducing skull-induced artifacts. Due to the difficulty of experimentally imaging the same rat brain with and without a skull, numerical simulation becomes a reasonable approach to studying skull-induced artifacts. In this work, we investigated the effects of skull-induced artifacts by simulating a grid of point targets inside the skull cavity and quantifying the pattern of skull-induced artifacts. With the capacity to automatically capture the artifact pattern given a large amount of paired training data, deep learning (DL) models can effectively reduce image artifacts in multiple modalities. This work explored the feasibility of using DL-based methods to reduce skull-induced artifacts in US imaging. Simulated data were used to train a U-Net-derived, image-to-image regression network. US channel data with artifact signals served as inputs to the network, and channel data with reduced artifact signals were the regression outcomes. Results suggest the proposed method can reduce skull-induced artifacts and enhance target signals in B-mode images.