Switchable Deep Beamformer For Ultrasound Imaging Using Adain

Abstract

In ultrasound (US) imaging, various adaptive beamforming methods have been proposed to improve the resolution and contrast-to-noise ratio of the delay and sum (DAS) beamformers. Unfortunately, they often require computationally expensive calculations and their performance degrades when the underlying model is not sufficiently accurate. Moreover, ultrasound images usually require various type of post filtration such as deblurring and despeckling, etc., which further increase the complexity of the system. Deep learning-based solutions provides a quick remedy to these issue; however, in the current technology, a separate beamformer should be trained and stored for each application, demanding significant scanner resources. To address this problem, here we propose a switchable deep beamformer that can produce various types of output such as DAS, speckle removal, deconvolution, etc., using a single network with a simple switch. In particular, the switch is implemented through Adaptive Instance Normalization (AdaIN) layers, so that distinct outputs can be generated by merely changing the AdaIN code. Experimental results using B-mode focused ultrasound confirm the efficacy of the proposed methods.