Graduate School of Science and Engineering, Chiba University, 1-33 Yayoicho, Inage, Chiba, 2638522, Japan.
Preeminent Medical Photonics Education and Research Center, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, 4313192, Japan.
J Med Ultrason (2001). 2024 Jan;51(1):5-16. doi: 10.1007/s10396-023-01354-3. Epub 2023 Oct 5.
Quantitative diagnosis of the degree of fibrosis progression is currently a focus of attention for fatty liver in nonalcoholic steatohepatitis (NASH). However, previous studies have focused on either lipid droplets or fibrotic tissue, and few have reported the evaluation of both in patients whose livers contain adipose and fibrous features. Our aim was to evaluate fibrosis tissue and lipid droplets in the liver.
We used an analytical method combining the multi-Rayleigh (MRA) model and a healthy liver structure filter (HLSF) as a technique for statistical analysis of the amplitude envelope to estimate fat and fibrotic volumes in clinical datasets with different degrees of fat and fibrosis progression.
Fat mass was estimated based on the non-MRA fraction corresponding to the signal characteristics of aggregated lipid droplets. Non-MRA fraction has a positive correlation with fat mass and is effective for detecting moderate and severe fatty livers. Progression of fibrosis was estimated using MRA parameters in combination with the HLSF. The proposed method was used to extract non-healthy areas with characteristics of fibrotic tissue. Fibrosis in early fatty liver suggested the possibility of evaluation. On the other hand, fat was identified as a factor that reduced the accuracy of estimating fibrosis progression in moderate and severe fatty livers.
The proposed method was used to simultaneously evaluate fat mass and fibrosis progression in early fatty liver, suggesting the possibility of quantitative evaluation for discriminating between lipid droplets and fibrous tissue in the early fatty liver.
目前，非酒精性脂肪性肝炎（NASH）中肝纤维化进展程度的定量诊断是研究的重点。然而，以前的研究主要集中在脂滴或纤维组织上，很少有研究报告评估同时含有脂肪和纤维特征的患者的这两个方面。我们的目的是评估肝脏中的纤维组织和脂滴。
我们使用了一种分析方法，结合多瑞利（MRA）模型和健康肝脏结构滤波器（HLSF），作为一种统计分析幅度包络的技术，用于对具有不同脂肪和纤维化进展程度的临床数据集进行脂肪和纤维化体积的估计。
基于对应于聚集脂滴信号特征的非 MRA 分数来估计脂肪质量。非 MRA 分数与脂肪质量呈正相关，对于检测中度和重度脂肪肝是有效的。纤维化进展是使用 MRA 参数与 HLSF 结合来估计的。该方法用于提取具有纤维组织特征的非健康区域。早期脂肪肝中的纤维化提示了评估的可能性。另一方面，脂肪被认为是降低中度和重度脂肪肝中纤维化进展估计准确性的一个因素。
该方法用于同时评估早期脂肪肝中的脂肪质量和纤维化进展，提示了定量评估早期脂肪肝中脂滴和纤维组织的可能性。
