Switchable Deep Beamformer

TL;DR

This paper introduces a switchable deep beamformer that uses AdaIN layers to generate multiple ultrasound image outputs with a single network, reducing resource needs and enhancing versatility.

Contribution

A novel switchable deep beamformer utilizing AdaIN layers enables multiple ultrasound image processing modes from one network, improving efficiency and flexibility.

Findings

Successfully produced various ultrasound images with one network

Confirmed effectiveness across multiple applications

Reduced resource requirements for ultrasound imaging

Abstract

Recent proposals of deep beamformers using deep neural networks have attracted significant attention as computational efficient alternatives to adaptive and compressive beamformers. Moreover, deep beamformers are versatile in that image post-processing algorithms can be combined with the beamforming. Unfortunately, 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 {\em 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 various output can be generated by merely changing the AdaIN code. Experimental results using B-mode focused ultrasound confirm the flexibility and efficacy of the proposed methods for various applications.