IEEE Trans Ultrason Ferroelectr Freq Control. 2023 Jul;70(7):625-635. doi: 10.1109/TUFFC.2023.3276634. Epub 2023 Jun 29.
Noninvasive imaging of microvascular structures in deep tissues provides morphological and functional information for clinical diagnosis and monitoring. Ultrasound localization microscopy (ULM) is an emerging imaging technique that can generate microvascular structures with subwavelength diffraction resolution. However, the clinical utility of ULM is hindered by technical limitations, such as long data acquisition time, high microbubble (MB) concentration, and inaccurate localization. In this article, we propose a Swin transformer-based neural network to perform end-to-end mapping to implement MB localization. The performance of the proposed method was validated using synthetic and in vivo data using different quantitative metrics. The results indicate that our proposed network can achieve higher precision and better imaging capability than previously used methods. Furthermore, the computational cost of processing per frame is 3-4 times faster than traditional methods, which makes the real-time application of this technique feasible in the future.
深层组织微血管结构的无创成像为临床诊断和监测提供形态和功能信息。超声定位显微镜（ULM）是一种新兴的成像技术，可以生成具有亚波长衍射分辨率的微血管结构。然而，ULM 的临床应用受到技术限制的阻碍，例如数据采集时间长、微泡（MB）浓度高和定位不准确。在本文中，我们提出了一种基于 Swin 变换器的神经网络来进行端到端映射，以实现 MB 定位。使用不同的定量指标，使用合成和体内数据验证了所提出方法的性能。结果表明，与以前使用的方法相比，我们提出的网络可以实现更高的精度和更好的成像能力。此外，处理每一帧的计算成本比传统方法快 3-4 倍，这使得该技术在未来的实时应用成为可能。
