发表机构
Singapore Institute of Technology（新加坡科技研究局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出物理驱动的目标条件深度波束形成器，通过特征级线性调制控制接收变迹，在单网络中实现分辨率-对比度自适应调节，并在PICMUS和CUBDL上零样本验证，显著提升gCNR和分辨率。
AI 中文摘要
平面波超声能够实现超快成像，但受限于接收孔径依赖的分辨率-对比度权衡。精细结构受益于高空间分辨率，而弥漫性组织和病变通常需要更高的对比度和杂波抑制。这些目标偏好不同的接收孔径配置，然而传统波束形成器在固定成像点工作。我们提出了一种物理驱动的、目标条件的深度波束形成器，能够在单个网络内实现可控的图像形成。从单平面波通道数据出发，网络预测空间变化的接收变迹权重，并通过特征级线性调制以期望的成像目标为条件。该条件变量明确地与物理定义的接收孔径配置相关联，并使用相应的相干平面波复合目标进行监督。可微分的、感兴趣区域感知的、物理引导的质量损失进一步在端到端训练期间促进目标特定的分辨率和对比度。该模型仅在涵盖多种目标形态和回声性的模拟通道数据上训练，并在PICMUS和CUBDL数据集上进行了零样本评估，包括未见过的探头几何形状。所提出的框架通常优于单平面波延迟求和重建，同时提供目标依赖的控制。在PICMUS上，与参考波束形成方法相比，所提出的方法将gCNR提高了高达16.8%，轴向分辨率提高了22%。此外，在对比度和分辨率导向目标之间切换可实现病变对比度高达4.9%和横向分辨率高达11.4%的可控变化。与性能最佳的学习型基线相比，所提出的方法将横向半峰全宽降低了5.9%，并将广义对比度噪声比提高了6.6%。
英文摘要
Plane-wave ultrasound enables ultrafast imaging but remains constrained by a receive-aperture-dependent resolution- contrast trade-off. Fine structures benefit from high spatial resolution, whereas diffuse tissue and lesions often require improved contrast and clutter suppression. These objectives favor different receive-aperture configurations, yet conventional beamformers operate at a fixed imaging point. We propose a physics-grounded, objective-conditioned deep beamformer that enables controllable image formation within a single network. From single-plane-wave channel data, the network predicts spatially varying receive-apodization weights, conditioned on the desired imaging objective through feature-wise linear modulation. The conditioning variable is explicitly tied to a physically defined receive-aperture configuration and supervised using the correspondingcoherent-plane-wave-compounding target. Differentiable, region-of-interest-aware, physics-guided quality losses further promote objective-specific resolution and contrast during end-to-end training. The model is trained exclusively on simulated channel data spanning diverse target morphologies and echogenicities, and evaluated zero-shot on the PICMUS and CUBDL datasets, including unseen probe geometries. The proposed framework generally improves upon single-plane-wave delay-and-sum reconstruction while providing objective-dependent control. On PICMUS, the proposed method improves gCNR by up to 16.8% and axial resolution by 22% compared with the reference beamforming method. Moreover, switching between the contrast- and resolution-oriented objectives enables controllable changes of up to 4.9% in lesion contrast and 11.4% in lateral resolution. Compared with the best-performing learning-based baselines, the proposed method reduces lateral full-width-at-half-maximum by 5.9% and improves generalized contrast-to-noise ratio by 6.6%.
