Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA.
Department of Mechanical Engineering, University of Rochester, Rochester, New York, USA.
Ultrasound Med Biol. 2020 Dec;46(12):3379-3392. doi: 10.1016/j.ultrasmedbio.2020.08.009. Epub 2020 Sep 8.
Fifty years of research on the nature of backscatter from tissues has resulted in a number of promising diagnostic parameters. We recently introduced two analyses tied directly to the biophysics of ultrasound scattering: the H-scan, based on a matched filter approach to distinguishing scattering transfer functions, and the Burr distribution for quantification of speckle patterns. Together, these analyses can produce at least five parameters that are directly linked to the mathematics of ultrasound in tissue. These have been measured in vivo in 35 rat livers under normal conditions and after exposure to compounds that induce inflammation, fibrosis, and steatosis in varying combinations. A classification technique, the support vector machine, is employed to determine clusters of the five parameters that are signatures of the different liver conditions. With the multiparametric measurement approach and determination of clusters, the different types of liver pathology can be discriminated with 94.6% accuracy.
五十年来，对组织背散射特性的研究产生了许多有前途的诊断参数。我们最近引入了两种直接与超声散射生物物理学相关的分析方法：基于匹配滤波器方法区分散射传递函数的 H 扫描，以及用于量化散斑模式的 Burr 分布。这两种分析方法结合起来可以产生至少五个与组织中超声的数学原理直接相关的参数。在正常条件下和暴露于不同组合诱导炎症、纤维化和脂肪变性的化合物后，在 35 只大鼠肝脏中进行了体内测量。支持向量机分类技术用于确定这五个参数的聚类，这些聚类是不同肝脏状况的特征。通过多参数测量方法和聚类的确定，可以以 94.6%的准确率区分不同类型的肝脏病理。
