Development of an electrical impedance tomography set-up for the quantification of mineralization in biopolymer scaffolds.

Objective. 3D cell cultures are becoming a fundamental resource forin-vitrostudies, as they mimic more closelyin-vivobehavior. The analysis of these constructs, however, generally rely on destructive techniques, that prevent the monitoring over time of the same construct, thus increasing the results variability and the resources needed for each experiment.Approach. In this work, we focus on mineralization, a crucial process during maturation of artificial bone models, and propose electrical impedance tomography (EIT) as an alternative non-destructive approach. In particular, we discuss the development of an integrated hardware/software system capable of acquiring experimental data from 3D scaffolds and reconstructing the corresponding conductivity maps. We also show how the same software can test how the measurement is affected by biological features such as scaffold shrinking during the culture.Main results. An initial validation, comprising the acquisition of both a non-conductive phantom and alginate/gelatin scaffolds with known calcium content will be presented, together with thein-silicostudy of a cell-induced mineralization process. This analysis will allow for an initial verification of the systems functionality while limiting the effects of biological variability due to cell number and activity.Significance. Our results show the potential of EIT for the non-destructive quantification of matrix mineralization in 3D scaffolds, and open to the possible long term monitoring of this fundamental hallmark of osteogenic differentiation in hybrid tissue engineered constructs.

Parallel Solver for Diffuse Optical Tomography on Realistic Head Models With Scattering and Clear Regions.

Diffuse optical tomography is an imaging technique, based on evaluation of how light propagates within the human head to obtain the functional information about the brain. Precision in reconstructing such an optical properties map is highly affected by the accuracy of the light propagation model implemented, which needs to take into account the presence of clear and scattering tissues. We present a numerical solver based on the radiosity-diffusion model, integrating the anatomical information provided by a structural MRI. The solver is designed to run on parallel heterogeneous platforms based on multiple GPUs and CPUs. We demonstrate how the solver provides a 7 times speed-up over an isotropic-scattered parallel Monte Carlo engine based on a radiative transport equation for a domain composed of 2 million voxels, along with a significant improvement in accuracy. The speed-up greatly increases for larger domains, allowing us to compute the light distribution of a full human head ( ≈ 3 million voxels) in 116 s for the platform used.