**1. Introduction**

Liver fibrosis develops as a sequel of chronic liver injury of various etiologies, including vi‐ ral infection, immunological reaction, and toxic and metabolic insults, and is characterized by the accumulation of extracellular matrix(ECM) components produced by fibroblast-like cells including activated stellate cells and myofibroblasts in the hepatic parenchyma. Hepat‐ ic fibrosis progresses towards cirrhosis, an end-stage liver injury, leading to hepatic failure, hepatocellular carcinoma, and finally death. Hepatitis C virus (HCV) infection is the most common cause of liver fibrosis. HCV infects approximately 170 million individuals world‐ wide according to a report from the

World Health Organization [1]. Liver biopsy has been considered the 'gold standard' meth‐ od for the evaluation of liver fibrosis in chronic hepatitis C [2]. However, liverbiopsy has some limitations, including its invasiveness, risk of complications, sampling error, variabili‐ ty in histopathological interpretation, and the reluctance of patients to subject to repeated examinations [3-11].Because of these disadvantages, there is a growing shift inclinical prac‐ tice to utilize or develop 'non-invasive'methodologies to evaluate the stage of liver fibrosis. In particular, liver stiffness measurement by Vibration-Controlled Transient Elastography (Fibroscan) has become establishedas an important modality. Recently we and other investi‐ gator reported the usefulness of real-time tissue elastography (RTE) for noninvasive, visual assessment of liver stiffness in patients with chronic hepatitis C [12.13]. RTE is a method in‐ tegrated in a sonography machine and developed in Japan for the visual assessment of tis‐ sue elasticity, based on a Combined Autocorrelation Method that calculates rapidly the relative hardness of tissue from the degree of tissue distortion and which displays this infor‐ mation as a color image [14]. This technology has already been proved to be diagnostically

© 2012 Morikawa; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

valuable in the breast cancer [15]. We show here the additional value of RTE, in comparsion to Fibroscanin patients with chronic liver disease.

RTE is carried out using a high quality ultrasound system (Hitachi AlokaMedical, Chiba, Ja‐ pan). The software uses a complex algorithm to process in a very short time all the data coming from the lesion as radiofrequency impulses and to minimize the artifacts due to lat‐ eral dislocations, allowing accurate measurement of the degree of tissue distortion. We used the Hitachi EUB-8500 and EUP-L52 Linear probe (3–7 MHz; Hitachi AlokaMedical) for RTE.

Real-Time Tissue Elastography and Transient Elastography for Evaluation of Hepatic Fibrosis

http://dx.doi.org/10.5772/52695

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**Figure 1.** The principle and procedure of image analyses for real-time tissue elastography.(A) When a spring is com‐ pressed, displacement in each section of the spring depends on the stiffness of that part of the spring: a soft section compresses more than a hard section. The strain distribution can be measured by differentiating the spatial displace‐ ment at each location. (B) The ROI was fixed to a rectangle of approximately 20-30 mm length x 20 mm breadth with a 400–600 mm2 area located 5-10 mm below the surface of the liver.left; RTE image, right; B-mode image. (C-D) The color-coded images from the ROI of the RTE were analyzed by the software Elasto\_ver1.5.1. The colors ranged from blue to red indicating the relative gradients from hardness to softness. The Mean and Standard deviation were calcu‐ lated by a histogram, which was generated by 256 stepwise grading derived from the color image. The Area and Com‐ plexity were calculated from the binary image. Area was derived from the percentage of white regions (asterisks, i.e. hard area). Complexity was calculated asperiphery2/Area. Median value of the data were recorded as representative

This system is currently commercially available for the diagnosis of mammary neoplasm. Patients were examined in a supine position with the right arm elevated above the head, and were instructed to hold their breath. The examination was performed on the right lobe of the liver through the intercostal space, and liver biopsy and Fibroscan also were per‐ formed at the same site. The RTE equipment displays two images simultaneously; one shows the regions of interest (ROI) as a colored area and the other indicates the conventional

of RTE parameters.
