Continuous Nondestructive Monitoring Method Using the Reconstructed Three-Dimensional Conductivity Images via GREIT for Tissue Engineering

Abstract

A continuous Nondestructive monitoring method is required to apply proper feedback controls during tissue regeneration. Conductivity is one of valuable information to assess the physiological function and structural formation of regenerated tissues or cultured cells. However, conductivity imaging methods suffered from inherited ill-posed characteristics in image reconstruction, unknown boundary geometry, uncertainty in electrode position, and systematic artifacts. In order to overcome the limitation of microscopic electrical impedance tomography (micro-EIT), we applied a 3D-specific container with a fixed boundary geometry and electrode configuration to maximize the performance of Graz consensus reconstruction algorithm for EIT (GREIT). The separation of driving and sensing electrodes allows us to simplify the hardware complexity and obtain higher measurement accuracy from a large number of small sensing electrodes. We investigated the applicability of the GREIT to 3D micro-EIT images via numerical simulations and large-scale phantom experiments. We could reconstruct multiple objects regardless of the location. The resolution was 5 mm3with 30 dB SNR and the position error was less than 2.54 mm. This shows that the new micro-EIT system integrated with GREIT is robust with the intended resolution. With further refinement and scaling down to a microscale container, it may be a continuous nondestructive monitoring tool for tissue engineering applications.

3D EIT image reconstruction with GREIT

“…By itself, 3D EIT imaging is not novel; early work by Rabbani and Kabir (1991) considered the volumetric sensitivity of EIT; 3D reconstruction was first shown by Metherall et al (1996) for difference EIT, but has since also been used in many absolute EIT algorithms. GREIT has also recently been extended to 3D by Ahn et al (2014). Given all this work on 3D EIT, it is surprising that almost all thoracic EIT imaging is still done with a single plane of electrodes.…”

“…Subsequent adaptations allowed reconstructions on arbitrary geometry [2]. Recently, Ahn et al [3] proposed and tested in a 360-electrode micro-EIT setup an extension of the GREIT algorithm to 3D. We describe a similar implementation contributed to EIDORS and investigate the algorithm's properties.…”

“…The original formulation and implementation was limited to cylindrical geometries with planar electrode arrangements, but subsequent adaptations facilitate reconstructions on arbitrary geometries (Grychtol et al 2012). Recently, Ahn et al (2014) proposed and tested in a 360-electrode micro-EIT setup an extention of the GREIT algorithm to 3D.…”

Optimization of Electrode Configuration for 3-D Brain EIT

“…The linear EIT image reconstruction can be represented by computing the reconstruction matrix R, which corresponds to the measurement y, in order to produce the reconstructed image x, as shown in equation ( 1) [27]:…”

Thoracic EIT in 3D: experiences and recommendations

“…Subsequent authors have extended this approach to a regularized Gauss-Newton inverse (e.g. Blue et al (2000) and Borsic et al (2010)) or the GREIT algorithm (Ahn et al 2014). Other approaches have been used, such as the D-bar scattering transform (Knudsen et al 2009).…”