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Non-Invasive Electrical Impedance Tomography for Multi-Scale Detection of Liver Fat Content

  • Author(s): Luo, Yuan
  • Abiri, Parinaz
  • Zhang, Shell
  • Chang, Chih-Chiang
  • Kaboodrangi, Amir H.
  • Li, Rongsong
  • Sahib, Ashish K.
  • Bui, Alex
  • Kumar, Rajesh
  • Woo, Mary
  • Li, Zhaoping
  • Packard, René Sevag
  • Tai, Yu-Chong
  • Hsiai, Tzung K.
  • et al.

Published Web Location

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5858172/
No data is associated with this publication.
Abstract

Introduction: Obesity is associated with an increased risk of nonalcoholic fatty liver disease (NAFLD). While Magnetic Resonance Imaging (MRI) is a non-invasive gold standard to detect fatty liver, we demonstrate a low-cost and portable electrical impedance tomography (EIT) approach with circumferential abdominal electrodes for liver conductivity measurements.Methods and Results: A finite element model (FEM) was established to simulate decremental liver conductivity in response to incremental liver lipid content. To validate the FEM simulation, we performed EIT imaging on an ex vivo porcine liver in a non-conductive tank with 32 circumferentially-embedded electrodes, demonstrating a high-resolution output given a priori information on location and geometry. To further examine EIT capacity in fatty liver detection, we performed EIT measurements in age- and gender-matched New Zealand White rabbits (3 on normal, 3 on high-fat diets). Liver conductivity values were significantly distinct following the high-fat diet (p = 0.003 vs. normal diet, n=3), accompanied by histopathological evidence of hepatic fat accumulation. We further assessed EIT imaging in human subjects with MRI quantification for fat volume fraction based on Dixon procedures, demonstrating average liver conductivity of 0.331 S/m for subjects with low Body-Mass Index (BMI < 25 kg/m²) and 0.286 S/m for high BMI (> 25 kg/m²).Conclusion: We provide both the theoretical and experimental framework for a multi-scale EIT strategy to detect liver lipid content. Our preliminary studies pave the way to enhance the spatial resolution of EIT as a marker for fatty liver disease and metabolic syndrome.

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