Abstract：
Ultrafast ultrasound is an emerging imaging modality derived from standard medical ultrasound. It allows for a high spatial resolution of 100 μm and a temporal resolution in the millisecond range with techniques such as ultrafast Doppler imaging. Ultrafast Doppler imaging has become a priceless tool for neuroscience, especially for visualizing functional vascular structures and navigating the brain in real time. Yet, the quality of a Doppler image strongly depends on experimental conditions and is easily subject to artifacts and deterioration, especially with transcranial imaging, which often comes at the cost of higher noise and lower sensitivity to small blood vessels. A common solution to better visualize brain vasculature is either accumulating more information, integrating the image over several seconds or using standard filter-based enhancement techniques, which often over-smooth the image, thus failing both to preserve sharp details and to improve our perception of the vasculature. In this study we propose combining the standard Doppler accumulation process with a real-time enhancement strategy, based on deep-learning techniques, using perceptual loss (PerceptFlow). With our perceptual approach, we bypass the need for long integration times to enhance Doppler images. We applied and evaluated our proposed method on transcranial Doppler images of mouse brains, outperforming state-of-the-art filters. We found that, in comparison to standard filters such as the Gaussian filter (GF) and block-matching and 3-D filtering (BM3D), PerceptFlow was capable of reducing background noise with a significant increase in contrast and contrast-to-noise ratio, as well as better preserving details without compromising spatial resolution.
摘要：
超快超声是源自标准医学超声的新兴成像模态。使用诸如超快多普勒成像的技术，它允许100μm的高空间分辨率和毫秒范围内的时间分辨率。超快多普勒成像已成为神经科学的无价工具，特别是用于可视化功能血管结构和实时导航大脑。然而，多普勒图像的质量在很大程度上取决于实验条件，并且容易出现伪影和恶化，尤其是经颅成像，这通常是以较高的噪声和对小血管较低的敏感性为代价的。更好地可视化脑血管系统的常见解决方案是积累更多信息，在几秒钟内整合图像或使用标准的基于滤波器的增强技术，这通常会过度平滑图像，因此，既不能保留清晰的细节，也不能改善我们对脉管系统的感知。在这项研究中，我们建议将标准多普勒累积过程与实时增强策略相结合，基于深度学习技术，使用感知损失(感知流)。用我们的感知方法，我们绕过了长积分时间来增强多普勒图像的需要。我们在小鼠大脑的经颅多普勒图像上应用并评估了我们提出的方法，优于最先进的过滤器。我们发现,与高斯滤波器（GF），块匹配和3-D滤波（BM3D）等标准滤波器相比，PerceptFlow能够降低背景噪声，并显着增加对比度和对比度噪声比，以及更好地保留细节而不影响空间分辨率。
