用端到端优化让超声探头用一半元件仍保持清晰成像。
End-to-end optimization of sparse ultrasound linear probes
- 联合优化探头稀疏布局与图像重建，融合物理模型与深度学习。
- 仿真显示仅用一半有效阵元，轴向和侧向分辨率仍保持良好。
- 适合想设计低成本、高性能超声设备的研究者或工程师。
超声成像面临图像质量与硬件复杂性之间的权衡，密集换能器是主要原因。稀疏阵列是一种常见解决方案。本文提出一种端到端优化框架，联合学习稀疏阵列配置与图像重建。该框架整合了可微分的成像形成模型、带有HARD直通思想估计器（STE）的选择掩码、展开的迭代软阈值算法（ISTA）去卷积，以及残差卷积神经网络（CNN）。目标函数结合了物理一致性（点扩散函数（PSF）和卷积成像模型）与结构保真度（对比度、旁瓣比（SLR）、熵和行多样性）。使用3.5 MHz探头的仿真结果表明，所学配置在仅使用一半有效元件的情况下，仍能保持轴向和侧向分辨率。这种基于物理引导的数据驱动方法，可在不牺牲图像质量的前提下实现紧凑、低成本的超声探头设计，并可扩展至三维体积成像。
原文摘要 · Abstract (English)
Ultrasound imaging faces a trade-off between image quality and hardware complexity caused by dense transducers. Sparse arrays are one popular solution to mitigate this challenge. This work proposes an end-to-end optimization framework that jointly learns sparse array configuration and image reconstruction. The framework integrates a differentiable Image Formation Model with a HARD Straight Thought Estimator (STE) selection mask, unrolled Iterative Soft-Thresholding Algorithm (ISTA) deconvolution, and a residual Convolutional Neural Network (CNN). The objective combines physical consistency (Point Spread Function (PSF) and convolutional formation model) with structural fidelity (contrast, Side-Lobe-Ratio (SLR), entropy, and row diversity). Simulations using a 3.5\,MHz probe show that the learned configuration preserves axial and lateral resolution with half of the active elements. This physics-guided, data-driven approach enables compact, cost-efficient ultrasound probe design without sacrificing image quality, and it is expandable to 3-D volumetric imaging.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。