PerceptFlow: Real-Time Ultrafast Doppler Image Enhancement Using Deep Convolutional Neural Network and Perceptual Loss.

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.