提出多阶段残差感知框架，提升超声应变弹性成像的稳定性与清晰度。
Multi-Stage Residual-Aware Unsupervised Deep Learning Framework for Consistent Ultrasound Strain Elastography
- 分阶段无监督学习，通过残差修正提升应变估计精度。
- 仿真数据信噪比达24.54，背景信噪比132.76，对比度显著提升。
- 适合临床医生用于病变区域更清晰的超声弹性图像分析。
超声应变弹性成像（USE）是一种评估组织力学特性的非侵入性成像技术，在多种临床应用中具有重要诊断价值。然而，其临床应用受限于组织去相关噪声、真实标签稀缺以及不同形变条件下的应变估计不一致。为此，我们提出MUSSE-Net，一种基于残差感知的多阶段无监督序列深度学习框架，旨在实现鲁棒且一致的应变估计。核心为提出的USSE-Net，一种端到端的多流编码器-解码器架构，可并行处理形变前后的射频信号序列，以估计位移场和轴向应变。该架构引入基于上下文感知互补特征融合（CACFF）的编码器、三重交叉注意力（TCA）瓶颈及基于交叉注意力融合（CAF）的序列解码器，并设计特定的一致性损失以保证不同形变水平下的时间一致性与应变稳定性。最终，通过MUSSE-Net框架中的二级残差精修阶段进一步提升精度并抑制噪声。在仿真数据、活体数据及孟加拉国工程与技术大学医疗中心（BUET）的私有临床数据集上进行广泛验证，MUSSE-Net表现优于现有无监督方法。在仿真数据上达到目标信噪比24.54、背景信噪比132.76、对比度信噪比59.81和弹性成像信噪比9.73；在BUET数据集中，生成的应变图具有更高病变-背景对比度和显著降噪效果，呈现临床可解读的应变模式。
原文摘要 · Abstract (English)
Ultrasound Strain Elastography (USE) is a powerful non-invasive imaging technique for assessing tissue mechanical properties, offering crucial diagnostic value across diverse clinical applications. However, its clinical application remains limited by tissue decorrelation noise, scarcity of ground truth, and inconsistent strain estimation under different deformation conditions. Overcoming these barriers, we propose MUSSE-Net, a residual-aware, multi-stage unsupervised sequential deep learning framework designed for robust and consistent strain estimation. At its backbone lies our proposed USSE-Net, an end-to-end multi-stream encoder-decoder architecture that parallelly processes pre- and post-deformation RF sequences to estimate displacement fields and axial strains. The novel architecture incorporates Context-Aware Complementary Feature Fusion (CACFF)-based encoder with Tri-Cross Attention (TCA) bottleneck with a Cross-Attentive Fusion (CAF)-based sequential decoder. To ensure temporal coherence and strain stability across varying deformation levels, this architecture leverages a tailored consistency loss. Finally, with the MUSSE-Net framework, a secondary residual refinement stage further enhances accuracy and suppresses noise. Extensive validation on simulation, in vivo, and private clinical datasets from Bangladesh University of Engineering and Technology (BUET) medical center, demonstrates MUSSE-Net's outperformed existing unsupervised approaches. On MUSSE-Net achieves state-of-the-art performance with a target SNR of 24.54, background SNR of 132.76, CNR of 59.81, and elastographic SNR of 9.73 on simulation data. In particular, on the BUET dataset, MUSSE-Net produces strain maps with enhanced lesion-to-background contrast and significant noise suppression yielding clinically interpretable strain patterns.
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。