[论文解读] Morphology-, Noise-, and Resolution-Robust Ultrasound Elasticity Imaging with Fourier Neural Operators

Heekyu Kim, Hugon LEe+2 more|arXiv (Cornell University)|Jan 21, 2026

Ultrasound Imaging and Elastography被引用 0

一句话总结

本文研究将傅里叶神经算子（FNO）应用于超声弹性成像，评估在形态、噪声和分辨率下的鲁棒性，并在仿真中显示FNO优于基线模型。

ABSTRACT

Ultrasound-based elasticity imaging is a non-invasive technique for estimating tissue stiffness fields from displacement fields obtained by comparing ultrasound signals before and after compression. While recent deep learning approaches have enabled faster and more accurate elasticity estimation compared to traditional methods, several challenges remain for clinical translation. In this study, we employ finite element simulations of free-hand palpation to investigate the applicability of the Fourier neural operator (FNO). Four practical scenarios were investigated: (1) prediction across diverse lesion morphologies, (2) generalization to cases with lesion counts differing from those in the training data, (3) robustness to noise in measured displacement fields, and (4) resilience to variations in ultrasound device resolution. Across these tasks, FNO consistently outperformed baseline models such as U-Net and DeepONet in predictive accuracy and generalization, while maintaining robustness under noise and resolution changes. Validated through in silico simulations, these findings demonstrate the potential of FNO as a framework that could facilitate translation of elasticity imaging toward clinical practice.

研究动机与目标

• 研究傅里叶神经算子在超声弹性成像中的可应用性。
• 评估在病灶形态和病灶数量变化下的泛化能力。
• 评估对噪声位移测量的鲁棒性。
• 评估对超声设备分辨率变化的韧性。

提出的方法

• 使用自由有限元仿真来生成数据（手动触诊）。
• 将傅里叶神经算子应用于将位移场映射为弹性场。
• 在不同场景下将FNO与U-Net和DeepONet进行比较。
• 测试对不同病灶形态和数量的泛化。
• 评估对测量噪声和分辨率变化的鲁棒性。

实验结果

研究问题

• RQ1 FNO能否在多样化的病灶形态下从位移场准确估计组织弹性？
• RQ2 当病灶数量与训练数据不同寻常时，FNO是否具有泛化性？
• RQ3 FNO对测量位移场的噪声是否鲁棒？
• RQ4 FNO对超声设备分辨率的变化是否鲁棒？

主要发现

• FNO在预测准确性和跨任务泛化方面始终优于基线模型（U-Net和DeepONet）。
• FNO在位移测量噪声下仍保持鲁棒性。
• FNO对超声设备分辨率的变化表现出韧性。
• 虚拟仿真验证了FNO在弹性成像临床转化中的潜力。