Abstract
Speckle patterns in ultrasound images often obscure anatomical details, leading to diagnostic uncertainty. Recently, various deep learning (DL)-based techniques have been introduced to effectively suppress speckle; however, their high computational costs pose challenges for low-resource devices, such as portable ultrasound systems. To address this issue, EdgeSRIE, which is a lightweight hybrid DL framework for real-time speckle reduction and image enhancement in portable ultrasound imaging, is introduced. The proposed framework consists of two main branches: an unsupervised despeckling branch, which is trained by minimizing a loss function between speckled images, and a deblurring branch, which restores blurred images to sharp images. For hardware implementation, the trained network is quantized to 8-bit integer precision and deployed on a low-resource system-on-chip (SoC) with limited power consumption. In the performance evaluation with phantom and in vivo analyses, EdgeSRIE achieved the highest contrast-to-noise ratio (CNR) and average gradient magnitude (AGM) compared with the other baselines (different 2-rule-based methods and other 4-DL-based methods). Furthermore, EdgeSRIE enabled real-time inference at over 60 frames per second while satisfying computational requirements (< 20K parameters) on actual portable ultrasound hardware. These results demonstrated the feasibility of EdgeSRIE for real-time, high-quality ultrasound imaging in resource-limited environments.
Abstract (translated)
超声图像中的斑点模式常常掩盖解剖细节，导致诊断不确定性。近年来，各种基于深度学习（DL）的技术被引入以有效抑制斑点噪声；然而，这些技术的高计算成本给资源有限的设备带来了挑战，如便携式超声系统。为了解决这一问题，提出了EdgeSRIE，这是一种轻量级混合DL框架，用于在便携式超声成像中实现实时斑点减少和图像增强。该框架主要由两个分支组成：一个无监督去斑点分支，通过最小化斑点图像之间的损失函数进行训练；以及一个去模糊分支，将模糊的图像恢复为清晰的图像。 为了硬件实现，经过训练的网络被量化到8位整数精度，并部署在低资源系统级芯片（SoC）上，该芯片具有有限的功耗。在使用仿体和体内分析的性能评估中，EdgeSRIE与其它基准方法相比，在对比噪声比（CNR）和平均梯度幅度（AGM）方面取得了最高成绩。这些其他基准方法包括不同的基于规则的方法和其他四种基于DL的方法。此外，EdgeSRIE能够在实际便携式超声设备上满足计算要求（参数数量少于20K）的同时实现每秒超过60帧的实时推断。 这些结果证明了在资源有限环境中进行实时高质量超声成像时，EdgeSRIE的可行性。
URL
https://arxiv.org/abs/2507.03937
