Abstract
This study presents a computed tomography (CT) image-guided electrical impedance tomography (EIT) method for medical imaging. CT is a robust imaging modality for accurately reconstructing the density structure of the region being scanned. EIT can detect electrical impedance abnormalities to which CT scans may be insensitive, but the poor spatial resolution of EIT is a major concern for medical applications. A cross-gradient method has been introduced for oil and gas exploration to jointly invert multiple geophysical datasets associated with different medium properties in the same geological structure. In this study, we develop a CT image-guided EIT (CEIT) based on the cross-gradient method. We assume that both CT scanning and EIT imaging are conducted for the same medical target. A CT scan is first acquired to help solve the subsequent EIT imaging problem. During EIT imaging, we apply cross gradients between the CT image and the electrical conductivity distribution to iteratively constrain the conductivity inversion. The cross-gradient based method allows the mutual structures of different physical models to be referenced without directly affecting the polarity and amplitude of each model during the inversion. We apply the CEIT method to both numerical simulations and phantom experiments. The effectiveness of CEIT is demonstrated in comparison with conventional EIT. The comparison shows that the CEIT method can significantly improve the quality of conductivity images.
Highlights
I T IS essential to create visual representations of the interior of the human body for clinical analysis and medical intervention
We extend the cross-gradient technology developed by Gallardo and Meju for the field of oil and gas exploration to medical imaging with the specific intent of developing computed tomography (CT)-guided electrical impedance tomography
In this study, inspired by geophysical imaging problems [38]–[43], we use a CT image to constrain the electrical impedance tomography (EIT) imaging problem and improve the resolution by applying a novel technology invented for geophysical imaging called crossgradient regularization, which helps maintain the similarity between different physical properties while allowing a different magnitude for each property value in the common imaging area [3], [44]–[47]
Summary
I T IS essential to create visual representations of the interior of the human body for clinical analysis and medical intervention. Several numerical methods have been employed for solving the theoretical forward modeling problem including the finite element method (FEM) [15], the finite volume method (FVM) [16], the finite difference method (FDM) [17], and the boundary element method (BEM) [18] These algorithms have been successfully applied to EIT forward simulations. In this study, inspired by geophysical imaging problems [38]–[43], we use a CT image to constrain the EIT imaging problem and improve the resolution by applying a novel technology invented for geophysical imaging called crossgradient regularization, which helps maintain the similarity between different physical properties while allowing a different magnitude for each property value in the common imaging area [3], [44]–[47]. The imaging quality is assessed systematically for both simulations and phantom experiments
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