超声成像中的阴影消除：基于可微仿真与辐射场分解
Shadow Reduction in Ultrasound Imaging Using Differentiable Simulation and Radiance Field Decomposition
- University of Oxford（牛津大学）
- University of Amsterdam（阿姆斯特丹大学）
- Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）
- Imperial College London（伦敦帝国理工学院）
- University of Copenhagen（哥本哈根大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
提出物理信息后处理方法RFlash，通过可微辐射场分解超声图像为衰减和散射图，实现衰减自适应重渲染以消除阴影，在胎儿脑部等数据上显著降低阴影差异并提升分割性能。
中文摘要 AI 辅助
骨骼及其他高衰减组织产生的声学阴影会遮蔽超声图像中具有临床意义的结构。在胎儿脑部成像中，颅骨引起的伪影会不成比例地降低靠近换能器（近端）一侧半球的图像质量，从而限制了对两个半球的对称评估。现有的校正方法需要原始扫描仪数据，对组织特性施加了限制性假设，或依赖可能产生解剖结构幻觉的生成模型。我们提出了RFlash，一种物理信息驱动的后处理方法，该方法利用图像形成的可微辐射场公式，将波束形成后的超声图像分解为显式的衰减图和散射强度图。随后，通过衰减自适应重渲染，消除每个深度信号对中间组织的依赖性，等效于将换能器虚拟地推进到组织中。在1,261个三维胎儿脑部体积、143个真实二维凸阵腹部扫描和1,200个模拟二维线性探头肝脏扫描中，RFlash比经典的Hughes-Duck衰减校正更有效地减少了与阴影相关的强度差异。对于在远端半球（远离换能器）训练并应用于近端半球的胎龄模型，预测误差相对于原始图像减少了5.1天（40%）。估计的衰减图还生成了阴影置信图，这些图比仅使用图像时更能改进随机森林骨阴影分割，并且比现有的神经置信图基线获得更高的SHAP重要性，表明其具有更高的物理一致性。RFlash既不需要硬件修改，也不需要访问原始扫描仪数据，并支持线性和凸阵探头的2D和3D采集，使其广泛适用，允许临床医生在已获得的扫描仪和图像上使用我们的方法。
英文摘要
Acoustic shadows from bone and other highly attenuating tissues obscure clinically important structures in ultrasound. In fetal brain imaging, skull-induced artefacts disproportionately degrade the hemisphere closer to the transducer (proximal), limiting symmetric assessment of the two hemispheres. Existing correction methods require raw scanner data, impose restrictive assumptions on tissue properties, or rely on generative models that may hallucinate anatomy. We present RFlash, a physics-informed post-processing method that decomposes beamformed ultrasound images into explicit attenuation and scatter-intensity maps using a differentiable radiance-field formulation of image formation. Attenuation-adaptive re-rendering then removes the dependence of the signal at each depth on the intervening tissue, equivalent to virtually advancing the transducer into the tissue. Across 1,261 3D fetal brain volumes, 143 real 2D curvilinear abdominal scans, and 1,200 simulated 2D linear-probe liver scans, RFlash reduces shadow-related intensity differences more effectively than classical Hughes-Duck attenuation correction. For a gestational-age model trained on the distal hemisphere (further from the transducer) and applied to the proximal hemisphere, prediction error decreases by 5.1 days (40%) relative to the original images. The estimated attenuation maps also yield shadow-confidence maps that improve random-forest bone-shadow segmentation over the image alone and receive greater SHAP importance than an existing neural confidence-map baseline, suggesting greater physical consistency. RFlash requires neither hardware modification nor access to raw scanner data and supports 2D and 3D acquisitions with linear and curvilinear probes, making it widely applicable allowing clinicians to use our method on their already acquired scanners and images.
