Modified multi-Rayleigh model-based statistical analysis of ultrasound envelope for quantification of liver steatosis and fibrosis

Figures

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  Fig. 1. Conceptual diagram of flow for determining the number of components in the MRA model

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  Fig. 2. Conceptual diagram showing generation of the HLSF. a Scatter plot of all ROIs of livers classified as both S0 and F0 (n = 11). b Two-dimensional probability density distribution of the fibrosis parameters. c HLSF is determined based on the average distribution in healthy livers. Red color indicates the HLSF

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  Fig. 3. Non-MRA parameter mapping on B-mode images and probability density of echo amplitude in non-MRA. B-mode images (a) and parametric images (b) show the area judged as non-MRA. Images (i), (ii), and (iii) were obtained in patients graded as having F0 and S0 (0%), S1 (30%), and S3 (90%) fatty liver, respectively. Representative probability density of echo amplitude is shown for areas classified as non-MRA (c)

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  Fig. 4. Relationship between non-MRA fraction and fat content in all livers (a) and mean values and standard deviation of non-MRA fraction for each fatty liver grade and level of fibrosis progression (b). In the comparison of non-MRA percentages for each fatty liver grade and fibrosis progression, there were statistically significant differences between S0 and S1 (*\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.05$$\end{document}p<0.05) and S0 and S3 (**\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.01$$\end{document}p<0.01), and between S1 and S3 (*\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.05$$\end{document}p<0.05) for F0 liver in…

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  Fig. 5. Representative results of MRA parameter mapping on B-mode images for grades F0–F4 in S0 livers. B-mode images (a), parametric images of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\alpha }_{\mathrm{H}}$$\end{document}αH (b), and parametric images of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\sigma }_{\mathrm{H}}^{2}/{\sigma }_{\mathrm{M}}^{2}$$\end{document}σH2/σM2 (c) are shown. The patients were classified as (i) F0, (ii) F1, (iii) F2, or (iv) F3 based on METAVIR score

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  Fig. 6. Non-healthy liver features extracted using HLSF. Representative B-mode images are shown for grades F0–F3 in S0 livers, in the same patients as in Fig. 4b. a Two-dimensional probability density distribution of the fibrosis parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\alpha }_{\mathrm{H}}$$\end{document}αH and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\sigma }_{\mathrm{H}}^{2}/{\sigma }_{\mathrm{M}}^{2}$$\end{document}σH2/σM2 with the polar coordinate system. Red lines show the edge of HLSF. b, c Parametric mapping of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\alpha }_{\mathrm{H}}$$\end{document}αH and…

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  Fig. 7. Mean values and standard deviation of non-healthy area fraction calculated using the HLSF method for each fatty liver grade and level of fibrosis progression. In the multiple comparison test, there was a statistically significant difference between F0 and F3 (*\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.05$$\end{document}p<0.05) and between F0 and F4 (*\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.05$$\end{document}p<0.05) in the S0 liver. In the S1 livers, there was a statistically significant difference among F0 and F1–F4 (*\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.05$$\end{document}p<0.05 or…