用Transformer模型提升超声弹性成像的清晰度与分割精度

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.