SW-ViT：一种用于连续多次推注超声剪切波弹性成像的带后置去噪器的时空视觉Transformer网络
SW-ViT: A Spatio-Temporal Vision Transformer Network with Post Denoiser for Sequential Multi-Push Ultrasound Shear Wave Elastography
AI总结：
提出SW-ViT，一种两阶段深度学习框架，结合CNN-时空视觉Transformer重建网络与后置去噪网络，从噪声超声剪切波弹性成像数据中重建高质量刚度图并同时生成分割掩膜，在模拟和体模数据上均取得优异性能。
摘要：
目的：超声剪切波弹性成像（SWE）通过映射组织硬度（与恶性肿瘤相关）在评估软组织病理方面展现出巨大潜力。传统SWE方法在估计组织弹性方面显示出前景，但其易受噪声干扰、依赖有限的训练数据，且无法同时生成分割掩膜，这些因素对准确性和可靠性构成了显著挑战。方法：本文提出SW-ViT，一种新颖的两阶段深度学习框架，用于SWE，该框架将基于CNN-时空视觉Transformer的重建网络与高效的基于Transformer的后置去噪网络相结合。第一阶段使用3D ResNet编码器，配备多分辨率时空Transformer块以捕获空间和时间特征，随后使用挤压激励注意力解码器重建2D刚度图。为解决数据限制，采用基于块的训练策略进行局部学习和重建。第二阶段，一个具有共享编码器和双解码器的去噪网络处理包含物和背景区域，以生成精细的刚度图和分割掩膜。混合损失结合区域、平滑度、融合和交并比（IoU）分量，确保重建和分割的改进。结果：在模拟数据上，我们的方法实现了PSNR 32.68 dB，CNR 46.78 dB，SSIM 0.995。在体模数据上，结果包括PSNR 21.11 dB，CNR 42.14 dB，SSIM 0.936。分割IoU值达到0.949（模拟）和0.738（体模），ASSD值分别为0.184和1.011。意义：SW-ViT从噪声SWE数据中提供稳健、高质量的弹性图估计，并具有明确的临床应用前景。
英文摘要：
Objective: Ultrasound Shear Wave Elastography (SWE) demonstrates great potential in assessing soft-tissue pathology by mapping tissue stiffness, which is linked to malignancy. Traditional SWE methods have shown promise in estimating tissue elasticity, yet their susceptibility to noise interference, reliance on limited training data, and inability to generate segmentation masks concurrently present notable challenges to accuracy and reliability. Approach: In this paper, we propose SW-ViT, a novel two-stage deep learning framework for SWE that integrates a CNN-Spatio-Temporal Vision Transformer-based reconstruction network with an efficient Transformer-based post-denoising network. The first stage uses a 3D ResNet encoder with multi-resolution spatio-temporal Transformer blocks that capture spatial and temporal features, followed by a squeeze-and-excitation attention decoder that reconstructs 2D stiffness maps. To address data limitations, a patch-based training strategy is adopted for localized learning and reconstruction. In the second stage, a denoising network with a shared encoder and dual decoders processes inclusion and background regions to produce a refined stiffness map and segmentation mask. A hybrid loss combining regional, smoothness, fusion, and Intersection over Union (IoU) components ensures improvements in both reconstruction and segmentation. Results: On simulated data, our method achieves PSNR of 32.68 dB, CNR of 46.78 dB, and SSIM of 0.995. On phantom data, results include PSNR of 21.11 dB, CNR of 42.14 dB, and SSIM of 0.936. Segmentation IoU values reach 0.949 (simulation) and 0.738 (phantom) with ASSD values being 0.184 and 1.011, respectively. Significance: SW-ViT delivers robust, high-quality elasticity map estimates from noisy SWE data and holds clear promise for clinical application.
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