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Estimation of four-dimensional CT-based imaging biomarker of liver fibrosis using finite element method
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Paper Abstract

This study developed the estimation method of the liver elasticity using the finite element method (FEM) based on the four-dimensional computed tomography (4DCT) images acquired for radiotherapy planning, and to evaluate the feasibility of estimated elasticity as a biomarker for diagnose liver fibrosis. Fifteen patients who underwent 4DCT images and gadoxetate-acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) were enrolled in this study. The displacement vector fields were calculated between 4DCT images at the end-inspiration and at the end-exhalation using deformable image registration (actual respiratory-induced displacement). Further, we simulated the displacement during respiration by using the FEM (simulated respiratory-induced displacement). The elasticity in each element of liver model was optimized minimalizing the error between actual and simulated respiratory-induced displacement. In Gd-EOB-DTPAenhanced MRI, liver-to-spleen signal intensity ratio (LSR) was calculated using the mean signal intensity for whole liver and spleen. The correlations with two serum biomarkers (APRI: aspartate-aminotransferase to platelet ratio index, FIB-4: Fibrosis-4 index) for elasticity and for LSR were evaluated. The elasticity were strong correlation with APRI-score (r= 0.82), and with FIB-4-score (r= 0.86). On the other hand, LSR were modelate correlation with APRI-score (r= 0.32), and with FIB-4-score (r= 0.30). The mean ± standard deviation of errors between actual and simulated respiratory-induced displacement in the liver model was 0.63 ± 0.41 mm. In this study, liver elasticity was estimated using the FEM and respiratory-induced liver motion obtained from 4DCT images. Furthermore, the estimated elasticity could be a feasible imaging biomarker for diagnose the various degrees of liver fibrosis.

Paper Details

Date Published: 10 March 2020
PDF: 8 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130J (10 March 2020); doi: 10.1117/12.2548280
Show Author Affiliations
Koya Fujimoto, Yamaguchi Univ. (Japan)
Takehiro Shiinoki, Graduate School of Medicine, Yamaguchi Univ. (Japan)
Yuki Yuasa, Graduate School of Medicine, Yamaguchi Univ. (Japan)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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