Speckle noise reduction on aligned consecutive ultrasound frames via deep neural network

Abstract

Despite the benefits of ultrasound (US) imaging systems for medical diagnosis and treatment, US images are prone to low resolution and contrast due to US’s inherent attributes, as well as affected by speckle noise that directly influences their quality. In retrospective studies, diverse filters have been applied to minimize the effects of speckle noise and enhance the quality of US images. In this article, we propose a method of enhancing US images inspired by synthetic aperture imaging, which provides high-resolution images by adding low-resolution images and measuring the probe’s movement. Our proposed method does not involve synthetic aperture imaging but compensates for the motion effect in the temporal dimension, aligns consecutive images, and stacks aligned images to suppress speckle noise and consequently enhance the resolution of US images. We exploited deep neural network (DNN) models to estimate motion parameters between consecutive US images. In a new database of US images, we also collected the images’ position-related information implicitly measured in inertial measurement units, which was exploited as a ground truth for motion parameters between consecutive images. Compared with other image-enhancing techniques involving conventional filters and DNN modalities, our method demonstrated superiority in enhancing the quality of US images. We also found that estimating motion parameters directly influenced the success of the image-stacking process. As in ablation studies in DNNs, we additionally investigated the effect of dropping some images in the temporal dimension, which revealed that contextual differences and excessive rates of movement in successive US images weakens the image-stacking process and thus the potential enhancement of US images.