Mathematical framework for abdominal electrical impedance tomography to assess fatness

TL;DR

This paper introduces a mathematical framework for abdominal electrical impedance tomography that estimates fat thickness by using a depth-based reconstruction method to improve boundary error handling, demonstrated through simulations.

Contribution

It develops a novel depth-based EIT reconstruction method with a special current pattern to accurately assess abdominal fat, addressing boundary geometry errors.

Findings

Effective boundary error reduction demonstrated in simulations

Accurate estimation of subcutaneous fat thickness achieved

Potential for non-invasive obesity assessment

Abstract

This paper presents a static electrical impedance tomography (EIT) technique that evaluates abdominal obesity by estimating the thickness of subcutaneous fat. EIT has a fundamental drawback for absolute admittivity imaging because of its lack of reference data for handling the forward modeling errors. To reduce the effect of boundary geometry errors in imaging abdominal fat, we develop a depth-based reconstruction method that uses a specially chosen current pattern to construct reference-like data, which are then used to identify the border between subcutaneous fat and muscle. The performance of the proposed method is demonstrated by numerical simulations using 32-channel EIT system and human like domain.