PURPOSE: To assess the utility of texture analysis of T1 and T2 maps for the detection of hepatic fibrosis in a murine model of hepatic fibrosis. MATERIALS AND METHODS: Following Institutional Animal Care and Use Committee approval, a dietary model of hepatic fibrosis was used and 15 ex vivo murine livers were examined. Images were acquired using a 30 mm bore 11.7T magnetic resonance imaging (MRI) scanner with a rapid acquisition with relaxation enhancement sequence. Texture analysis was then employed, extracting texture features including histogram-based, gray-level co-occurrence matrix-based (GLCM), gray-level run-length-based features (GLRL), gray-level gradient matrix (GLGM), and Laws' features. Areas under the curve (AUCs) were then calculated to determine the ability of texture features to detect hepatic fibrosis. RESULTS: Texture analysis of T1 maps identified very good to excellent discriminators of hepatic fibrosis within the histogram and GLGM categories. Histogram feature interquartile range (IQR) achieved an AUC value of 0.90 (P < 0.0001) and GLGM feature variance gradient achieved an AUC of 0.91 (P < 0.0001). Texture analysis of T2 maps identified very good to excellent discriminators of hepatic fibrosis within the histogram, GLCM, GLRL, and GLGM categories. GLGM feature kurtosis was the best discriminator of hepatic fibrosis, achieving an AUC value of 0.90 (P < 0.0001). CONCLUSION: This study demonstrates the utility of texture analysis for the detection of hepatic fibrosis when applied to T1 and T2 maps in a murine model of hepatic fibrosis and validates the potential use of this technique for the noninvasive, quantitative assessment of hepatic fibrosis. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:250-259.
PURPOSE: To assess the utility of texture analysis of T1 and T2 maps for the detection of hepatic fibrosis in a murine model of hepatic fibrosis. MATERIALS AND METHODS: Following Institutional Animal Care and Use Committee approval, a dietary model of hepatic fibrosis was used and 15 ex vivo murine livers were examined. Images were acquired using a 30 mm bore 11.7T magnetic resonance imaging (MRI) scanner with a rapid acquisition with relaxation enhancement sequence. Texture analysis was then employed, extracting texture features including histogram-based, gray-level co-occurrence matrix-based (GLCM), gray-level run-length-based features (GLRL), gray-level gradient matrix (GLGM), and Laws' features. Areas under the curve (AUCs) were then calculated to determine the ability of texture features to detect hepatic fibrosis. RESULTS: Texture analysis of T1 maps identified very good to excellent discriminators of hepatic fibrosis within the histogram and GLGM categories. Histogram feature interquartile range (IQR) achieved an AUC value of 0.90 (P < 0.0001) and GLGM feature variance gradient achieved an AUC of 0.91 (P < 0.0001). Texture analysis of T2 maps identified very good to excellent discriminators of hepatic fibrosis within the histogram, GLCM, GLRL, and GLGM categories. GLGM feature kurtosis was the best discriminator of hepatic fibrosis, achieving an AUC value of 0.90 (P < 0.0001). CONCLUSION: This study demonstrates the utility of texture analysis for the detection of hepatic fibrosis when applied to T1 and T2 maps in a murine model of hepatic fibrosis and validates the potential use of this technique for the noninvasive, quantitative assessment of hepatic fibrosis. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:250-259.