Liver Fibrosis Scoring Using Ultrasound Backscattering Statistical Parametric Imaging: Theoratical and Clinical Study

• Main Authors: Feng-Cheng Chu, 朱峰正
• Other Authors: 張建成
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/45174107440530619808

碩士 === 國立臺灣大學 === 應用力學研究所 === 100 === The strategy of assessing and identifying early stage liver fibrosis has been viewed as an important issue in modern medicine. Liver histological diagnosis based on biopsy is the gold standard for liver fibrosis assessments nowadays, but sampling errors may occur when using this method. Thus, researchers have focused on developing non-invasive diagnosing method as a tool for the assessment. Ultrasonic technology has been widely applied in various fields, and has become the front-line non-invasive diagnosing tool in clinical medicine. Traditional ultrasound gray-scale image is a kind of qualitative image, and cannot provide the information of scatterers inside the tissue. Therefore, development of quantitative imaging or parameter has gradually become the mainstream. For the above reasons, we performed quantitative analysis of liver B-scan signal on patients with different stage of liver fibrosis by using our algorithm, and investigate the quantitative parameters along with the severity of fibrosis in this study. The characteristic of ultrasound speckle pattern in B-scan image, which results from the wave interference phenomenon of backscattering signal, was considered to be associated with the density of scatterers in tissue, which can be used in differentiating between healthy and diseased tissues. In this study, clinical ultrasound scanning signals were obtained by medical ultrasound equipment. Backscattering signals were described by Nakagami statistical distribution, and a Nakagami-model-based image has been calculated. Five kinds of quantitative parameters including Nakagami parameter, texture properties, mean intensity and attenuation coefficient, were also introduced for assessing the degree of fibrosis. Analysis results showed that the global statistics of backacattered signal changed from a Rayleigh distribution to a pre-Rayleigh distribution when the fibrosis score increased. It means that Nakagami image can be used for distinguishing different degrees of fibrosis. Calculation results of quantitative parameters revealed that Nakagami parameter decreased with the fibrosis score, and has outstanding performance in scoring early stage fibrosis (AUC F≥1:0.96、AUC F≥2:0.95、AUC F=3:0.97), while other parameters showed limited performance in staging fibrosis. In order to further understand variables that affect the calculation of Nakagami parameter, several investigations had been done in this study, including the size effect of region of interest (ROI), the effect of scanning position and fatty liver. Results showed that the optimum size of ROI lies between 7 times pulselength and 9 times pulselength. In addition, Nakagami parameter has better performance when setting ROI-size to 8 times pulselength. Analysis results also showed that no significant statistical deviation exists in calculation when changing scanning position in human liver. It also found that the brightness of scanning image is higher in patients with fatty liver, caused higher calculation results of Nakagami parameter, which leads to the failure of reflecting scatterer properties in the tissue. Last but not least, the ACRA method proposed by our lab provides a quantitative parameter to represent the degree of difference between normal and abnormal tissue. And results of the method demonstrated that ACRA parameter is highly correlated to the stage of early fibrosis. It is concluded that current findings of this study has great potential and clinical application value in diagnosing liver fibrosis.