发表机构
University of North Carolina at Chapel Hill; Johns Hopkins University（北卡罗来纳大学教堂山分校; 约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对资源受限超声成像中稀疏数据导致的栅瓣伪影问题，提出物理引导的多目标深度学习框架，结合射频域与波束形成域损失及随机跳跃掩蔽，实现高保真插值，平均SSIM约0.95。
AI 中文摘要
超声成像日益面向便携式、床旁和可穿戴设备，在这些场景中，功耗、带宽和硬件复杂度的限制往往要求在时空扫描中采用稀疏数据采集。然而，使用稀疏数据进行图像重建，在相干波束形成过程中可能引入不足的相位信息，导致栅瓣伪影，从而降低成像对比度分辨率。我们提出了一种物理引导、数据驱动的框架，用于稀疏到密集的射频（RF）重建，该框架使训练与下游图像形成过程对齐。我们的方法采用混合监督方案训练端到端插值网络，该方案结合了射频域和波束形成域损失，并使用指数移动平均（EMA）来稳定多目标训练。为了提高在不同采集布局下的泛化能力，我们还引入了一种随机跳跃掩蔽策略，该策略在训练期间改变稀疏模式，使单一模型能够处理不同的抽取因子和不规则通道配置。我们在一个独立的测试集上，使用重建图像与真实波束形成图像之间的平均结构相似性指数（SSIM）来评估该框架。在抽取因子从×2到×13的范围内，最佳配置的平均SSIM保持在0.95左右。总体而言，结果表明，在不同采集条件下，射频重建和波束形成后图像质量均有一致的提升。该方法通过在时空域允许更稀疏的扫描，实现了在资源受限环境下稳健、高质量的超声成像。
英文摘要
Ultrasound imaging increasingly targets portable, point-of-care, and wearable settings where constraints on power, bandwidth, and hardware complexity often necessitate sparse data acquisition in spatiotemporal scanning. However, image reconstruction using the sparse data can introduce insufficient phase information in coherent beamforming process, resulting in grating-lobe artifacts that degrade imaging contrast resolution. We present a physics-guided, data-driven framework for sparse-to-dense radio-frequency (RF) reconstruction that aligns training with downstream image formation. Our approach trains an end-to-end interpolation network using a hybrid supervision scheme that combines an RF-domain and a beamforming-domain loss with exponential moving average (EMA) to stabilize the multi-objective training. To improve generalization under variable acquisition layouts, we also introduce a random-skip masking strategy that varies sparsity patterns during training so a single model can handle diverse decimation factors and irregular channel configurations. We evaluate the framework on a held-out test set using the mean structural similarity index measure (SSIM) between reconstructed and ground-truth beamformed images. Across decimation factors $\times 2$ to $\times 13$, the best-performing configuration maintains mean SSIM around 0.95. Overall, the results show consistent gains in RF reconstruction and post-beamforming image quality across diverse acquisition conditions. This approach enables robust, high-quality ultrasound imaging at resource-constrained settings by allowing more sparse scanning in spatiotemporal domain.
CommentsSubmitted to the Journal of Computational Design and Engineering
