Anatomically constrained electrical impedance tomography for three-dimensional anisotropic bodies

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Electromagnetics

Background:

• Anisotropy significantly impacts Electrical Impedance Tomography (EIT) reconstructions, especially in complex geometries.
• Previous studies highlighted anisotropy's importance in 2D EIT, necessitating 3D investigations.
• Accurate conductivity distribution is vital for understanding physiological processes and disease states.

Purpose of the Study:

• To develop and present a method for static reconstruction of anisotropic conductivity distributions in three-dimensional (3-D) space.
• To anatomically constrain conductivity reconstructions using Magnetic Resonance Imaging (MRI) data.
• To validate the method's performance and sensitivity in a realistic canine torso model.

Main Methods:

• Utilizing a nonlinear-indirect approach with gradient-type algorithms for conductivity value determination.
• Solving the forward problem iteratively using the finite element method (FEM).
• Employing Magnetic Resonance Imaging (MRI) data to anatomically guide the reconstruction of conductivity boundaries.

Main Results:

• Successfully reconstructed the 3-D anisotropic conductivity profile of a canine torso.
• Demonstrated the computational performance and accuracy of the developed reconstruction method.
• Analyzed the sensitivity of prediction error to various parameters, including the use of an intracavity catheter for improved interior conductivity extraction.

Conclusions:

• The presented 3-D EIT reconstruction method effectively incorporates anisotropy and anatomical information.
• The finite element method and MRI-constrained approach provide a robust framework for anisotropic conductivity imaging.
• Intracavity catheters show promise for enhancing the resolution of interior conductivity measurements in EIT.