Abstract
In Ultrasound Localization Microscopy (ULM), achieving high-resolution images relies on the precise localization of contrast agent particles across consecutive beamformed frames. However, our study uncovers an enormous potential: The process of delay-and-sum beamforming leads to an irreversible reduction of Radio-Frequency (RF) data, while its implications for localization remain largely unexplored. The rich contextual information embedded within RF wavefronts, including their hyperbolic shape and phase, offers great promise for guiding Deep Neural Networks (DNNs) in challenging localization scenarios. To fully exploit this data, we propose to directly localize scatterers in RF signals. Our approach involves a custom super-resolution DNN using learned feature channel shuffling and a novel semi-global convolutional sampling block tailored for reliable and accurate localization in RF input data. Additionally, we introduce a geometric point transformation that facilitates seamless mapping between B-mode and RF spaces. To validate the effectiveness of our method and understand the impact of beamforming, we conduct an extensive comparison with State-Of-The-Art (SOTA) techniques in ULM. We present the inaugural in vivo results from an RF-trained DNN, highlighting its real-world practicality. Our findings show that RF-ULM bridges the domain gap between synthetic and real datasets, offering a considerable advantage in terms of precision and complexity. To enable the broader research community to benefit from our findings, our code and the associated SOTA methods are made available at this https URL.
Abstract (translated)
在超声波定位显微镜(ULM)中,获得高分辨率图像依赖于连续波形帧中均匀定位 contrast agent 颗粒的位置。然而,我们的研究揭示了一个巨大的潜力:延迟和相加的波形编码过程会导致射频数据 irreversible 减少,而对其定位的影响则在很大程度上未被探索。射频前端中的丰富上下文信息,包括其椭圆形状和相位,为在挑战性的定位场景中引导深度神经网络(DNN)提供了巨大的潜力。为了充分利用这些数据,我们提议直接定位射频信号中的散射剂。我们的方案涉及使用学习的特征通道交换自定义的高性能分辨率 DNN 和一个专门设计的全新半全局卷积采样块,以在射频输入数据中可靠且准确地定位。此外,我们引入了一种几何点变换,以方便在 B 模式和射频空间之间的无缝映射。为了验证我们的方法的有效性并理解波形编码的影响,我们进行了广泛的比较 ULM 中的最新技术。我们呈现了从射频训练的 DNN 中提取的最初的 vivo 结果,强调了其实际实用性。我们的研究结果表明,RF-ULM 跨越了合成数据和真实数据数据集之间的领域差距,在精度和复杂性方面提供了相当大的优势。为了让更多的人受益于我们的研究结果,我们的代码和相关的最新技术方法在此 https URL 上提供。
URL
https://arxiv.org/abs/2310.01545
