In vivo Quantitative Assessment of Hepatic Fibrosis by Ultrasonic Backscattering and Statistical Model

Abstract

In general the liver biopsy is the most accurate method to assess liver fibrosis The liver biopsy is a procedure to remove a small piece from liver tissue and it is invasive that can cause many complications Besides rats are widely used in liver fibrosis experiments and a lot of rats are sacrificed to verify assess the liver fibrosis level and reduced the sampling errors Therefore ultrasound and statistical parameters were used to assess the liver fibrosis as a non-invasive method in this study Sprague Dawley rats were induced liver fibrosis by CCl4 A 7 5MHz single element transducer was used in ultrasound system Nakagami parameter and IB were used to quantitatively assess the liver fibrosis as a non-invasive method B-mode images of mild and severe groups were significantly brighter than control group but the difference of brightness between mild and severe groups were not significant IB of control group was –131 9±3 3dB mild group was –117 3±0 8dB and severe group was –114 0±2 5dB The p-value of IB between control group and mild group was small than 0 01 when the p-value of IB between mild group and severe group was small than 0 05 IB was the strength of backscattering signals which was easily affected by ultrasound system and the angle of incident signals Nakagami statistical parameter could indicate the scatterer properties by ultrasonic backscattering signals Nakagami parameter of control group was 0 58±0 03 mild group was 0 78±0 02 and severe group was 0 83±0 02 The scatterer distribution of control group mild group and severe group were pre-Rayleigh distribution The p-value of Nakagami parameter between control group and mild group was small than 0 01 when the p-value of Nakagami parameter was small than 0 05 Nakagami images could show the local scatterer properties which B-mode images could not In the end B-mode images are still widely used in medical field so the Nakagami images can provide the local information to B-mode images and make the more accurate assessment of liver fibrosis stages

Date of Award	2017 Aug 28
Original language	English
Supervisor	Shyh-Hau Wang (Supervisor)