BrainPuzzle: a new data-driven method for ultrasound brain imaging

Abstract

Current ultrasound imaging techniques face challenges in producing clear brain images, primarily due to the contrast in sound velocity between the skull and brain tissues, and the difficulty in effectively coupling large probes with skulls. The reverse time migration (RTM) technique, known for its effectiveness in the geophysics community, is utilized to address this coupling issue. In addition, we propose the use of smaller probes capable of generating limited ultrasound brain image fragments from various angles. Subsequently, we have developed a new brain imaging method, termed BrainPuzzle, to restore brain images from these limited fragments. Unlike traditional transformer-based image generation models, BrainPuzzle not only uses a transformer to recognize and rearrange the fragments into their correct positions but also integrates a graph convolutional network (GCN) to automatically capture the spatial relationships among the fragments, thereby enhancing the model's capabilities. Furthermore, we introduce the concept of using the RTM method to generate these ultrasound brain image fragments. The experimental results based on two distinct sets of generated datasets, demonstrate the exceptional performance of the proposed method in reconstructing the complete brain images from the fragments of ultrasound brain images.