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Effective depth expansion for reliable fatty liver assessment using a double Nakagami distribution model

机译:使用双重Nakagami分布模型进行有效的深度扩展以进行可靠的脂肪肝评估

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Quantitative ultrasound (QUS) methods have been widely used for soft tissue characterization. Spatial resolution (i.e., ultrasound frequency) is an important factor for QUS methods. In our previous study, we proposed double Nakagami distribution (DND) model for the analysis of fatty liver and high frequency ultrasound (HFU) which allows finer-resolution QUS. Healthy liver structure filter (HLSF) classified each ROI based on the DND model parameter distribution which acquired from healthy liver samples. This approach was able to successfully diagnose fatty livers (>20 % steatosis percentage) in a dataset of 12 livers ranging from 0 to 90 % steatosis. This study proposed a compensation method to expand effective depth range of HLSF based on DND model using HFU measurement. Radio-frequency data was experimentally acquired from 12 excised rat livers (three healthy (0 % of hepatocytes contain lipid droplets) and nine fatty (10 to 70 %)). Healthy liver structure filter (HLSF) classified each ROI based on the DND model parameter distribution which acquired from healthy liver samples. The functions of the depth-dependent Nakagami parameters were obtained by fitting the modified Gaussian distribution to the Nakagami parameters obtained from the three normal liver samples. HLSF($x$) was constructed using healthy liver datasets from focal depth - 0.5 mm to focal deplth + 3.5 mm in 1 mm interval. The filter applied to estimated DND parameters at the same depth. For comparison, the conventional method used a fixed value of the Nakagami parameter for DND model parameter estimation and HLSF constructed at focal depth. Depth dependent of the Nakagami parameter and HLSF decreased the depth dependency of DND model parameter. AUROC classifying over than 15 % steatosis progress improved the performance at a distance from focal depth of +3.5 mm (0.64 to 0.86). The proposed method expanded reliable QUS (area under the receiver operating characteristic > 0.85) depth range by 250 % against half of depth of field and demonstrate QUS can be used reliably with clinical HFU.
机译:定量超声(QUS)方法已被广泛用于软组织表征。空间分辨率(即超声频率)是QUS方法的重要因素。在我们先前的研究中,我们提出了双Nakagami分布(DND)模型来分析脂肪肝和高频超声(HFU),从而可以实现更高分辨率的QUS。健康肝脏结构过滤器(HLSF)根据从健康肝脏样本中获得的DND模型参数分布对每个ROI进行分类。这种方法能够成功诊断出脂肪肝(大于20%的脂肪变性百分比)的12个肝脏,其脂肪变性为0%至90%。这项研究提出了一种补偿方法,该方法基于使用HFU测量的DND模型来扩展HLSF的有效深度范围。从12个切除的大鼠肝脏(3个健康的肝脏(0%的肝细胞含有脂质滴)和9个脂肪的脂肪(10%到70%))通过实验获得了射频数据。健康肝脏结构过滤器(HLSF)根据从健康肝脏样本中获得的DND模型参数分布对每个ROI进行分类。通过将修改后的高斯分布拟合到从三个正常肝脏样本获得的Nakagami参数,可以获得深度相关的Nakagami参数的功能。 HLSF( $ x $ )是使用健康的肝脏数据集构造而成的,其焦深-0.5 mm至焦深+ 3.5 mm(以1 mm为间隔)。过滤器在相同深度处应用于估计的DND参数。为了进行比较,传统方法使用Nakagami参数的固定值进行DND模型参数估计和在焦深处构造的HLSF。 Nakagami参数和HLSF的深度依赖性降低了DND模型参数的深度依赖性。 AUROC分类超过15%的脂肪变性进展,在距焦深+3.5毫米(0.64至0.86)的距离处改善了性能。所提出的方法将可靠的QUS(在接收器工作特性大于0.85的区域)的深度范围扩大了250%(相对于一半的景深),并证明了QUS可以可靠地用于临床HFU。

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