发表机构
Stanford University School of Medicine; University of California San Diego; University of Rochester（斯坦福大学医学院; 加州大学圣地亚哥分校; 罗切斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种结合2D自相关与匹配滤波的自适应频率估计器，用于改进H-scan超声组织表征，在抑制均匀区域噪声的同时保持边界清晰，并通过模拟和体内数据验证其优于传统方法。
AI 中文摘要
H-scan是一种有前景的定量超声技术，它估计背向散射信号的频率内容，并将估计的频率映射到红/蓝颜色尺度上，以反映潜在的组织特性。尽管它依赖于针对不同频率调谐的匹配滤波器，但超声的宽频谱带宽会产生噪声大、颗粒状的显示。在这里，我们引入了一种自适应频率估计器，旨在抑制均匀区域内的噪声，同时保持组织边界处的锐度。该方法结合了2D自相关与匹配滤波器。在第一阶段，基于匹配滤波器的估计产生频率空间分布的先验图。这些估计的局部异质性随后定义了一个2D加权函数，指导第二阶段估计。借鉴Loupas血流速度估计器的概念，我们在2D空间核上应用自相关来恢复轴向频率分量，采用考虑核内空间频率分布的加权求和。我们使用Field II模拟和来自人类脂肪肝受试者的体内数据，将所提出的估计器与传统方法（包括短时傅里叶变换、H-scan匹配滤波器和标准自相关）进行了基准测试。在模拟中，我们的自适应估计器减少了均匀区域内的噪声纹理，同时保留了清晰的边界描绘，而其他估计器只能实现这两个目标之一。应用于体内人肝脏时，该估计器通过降低噪声和增强脂肪肝与邻近胆囊及皮肤层的区分度，提高了H-scan图像质量。
英文摘要
H-scan is a promising quantitative ultrasound technique that estimates the frequency content of backscattered signals and maps the estimated frequencies onto a red/blue color scale to reflect underlying tissue properties. Although it relies on matched filters tuned to different frequencies, the broad spectral bandwidth of ultrasound produces noisy, granular displays. Here, we introduce an adaptive frequency estimator designed to suppress the noise within homogeneous regions while preserving sharpness across tissue boundaries. The method combines 2D autocorrelation with a matched filter. In the first stage, a matched-filter-based estimation yields an a priori map of the spatial distribution of frequencies. The local heterogeneity of these estimates then defines a 2D weighting function that guides a second estimation stage. Drawing on the concept of Loupas's blood velocity estimator, we apply autocorrelation over a 2D spatial kernel to recover the axial frequency components, employing a weighted summation that accounts for the spatial frequency distribution within the kernel. We benchmarked the proposed estimator against conventional approaches, including the short-time Fourier transform, the H-scan matched filter, and standard autocorrelation, using both Field II simulations and in vivo data from human subjects with hepatic steatosis. In simulation, our adaptive estimator reduced the noisy texture in homogeneous regions while retaining clear boundary delineation, whereas the other estimators could achieve only one of these objectives. Applied to the in vivo human liver, the estimator improved H-scan image quality by lowering noise and enhancing the discrimination of steatotic liver from adjacent gallbladder and skin layers.
