3D U-Net3+ Based Microbubble Filtering for Ultrasound Localization Microscopy

Ultrasound localization microscopy (ULM) is an innovative imaging technique that employs microbubbles (MBs) to improve the spatial resolution of ultrasound (US) imaging. Accurately extracting the MB signals from the original ultrasound data is essential for successful ULM. Traditional MB filtering methods, such as SVD, have high complexity and computational intensity. Due to the sensitivity to spatiotemporal information, 3D convolutional neural networks (3D CNN) have been utilized in MB filtering. However, the large number of parameters in 3D convolutional layers and complex network architectures affect the real-time performance of ULM. To optimize the network structure of 3D CNN and reduce parameters, this study proposes a novel MB filtering method based on 3D CNN and U-Net3+ named 3D U-Net3+. It adopts full-scale connection strategy to reduce network parameters, while combining low-level semantics and high-level semantics to capture fine-grained semantics and coarse-grained semantics at full scale. The experimental results demonstrate that the proposed MB filtering method can effectively preserve the spatiotemporal information of MBs in ultrasound sequence images. The SSIM and PSNR values of the MB image processed by the proposed method achieve 0.9141 and 30.881 dB, respectively. The obtained ULM image shows the microvessels as small as 20μm,