Optimal Region-of-Interest Settings for Tissue Characterization Based on Ultrasonic Elasticity Imaging

Pathologic changes in arterial walls significantly influence their mechanical properties. We have developed a correlation-based method, the phased tracking method, for measurement of the regional elasticity of the arterial wall. Using this method, elasticity distributions of lipids, blood clots, fibrous tissue and calcified tissue were measured by in-vitro experiments of excised arteries (mean ± SD: lipids, 89 ± 47 kPa; blood clots, 131 ± 56 kPa; fibrous tissue, 1022 ± 1040 kPa; calcified tissue, 2267 ± 1228 kPa). It was found that arterial tissues can be classified into soft tissues (lipids and blood clots) and hard tissues (fibrous tissue and calcified tissue) on the basis of their elasticity. However, there are large overlaps between elasticity distributions of lipids and blood clots and those of fibrous tissue and calcified tissue. Thus, it was difficult to differentiate lipids from blood clots and fibrous tissue from calcified tissue by setting a threshold for a single elasticity value. Therefore, we previously proposed a tissue classification method using the elasticity distribution in each small region. In this method, the elasticity distribution of each small region of interest (ROI) (not a single pixel) in an elasticity image is used to classify lipids, blood clots, fibrous tissue and calcified tissue by calculating the likelihood function for each tissue. In the present study, the optimum size of the ROI and threshold T_o for the likelihood function were investigated to improve the tissue classification. The ratio of correctly classified pixels to the total number of classified pixels was 29.8% when the size of a small region was 75 μm × 300 μm (a single pixel). The ratio of correctly classified pixels became 35.1% when the size of a small region was 1,500 μm × 1,500 μm (100 pixels). Moreover, a region with an extremely low likelihood with respect to all tissue components was defined as an unclassified region by setting threshold T_o for the likelihood function to 0.21. The tissue classification of the arterial wall was improved using the elasticity distribution of a small region whose size was larger than the spatial resolution (800 μm × 600 μm) of ultrasound. In this study, the arteries used in construction of the elasticity databases were classified into each tissue using the constructed elasticity databases. Other arteries, which are not used for constructing the elasticity databases, should be classified in future work to thoroughly show the effectiveness of the proposed method. (E-mail: hkanai@ecei.tohoku.ac.jp).