Search arXiv⌕ Search

arXiv · 2610.22944

Implementing the EIT complete electrode model through Robin boundary conditions

Abstract

Objective. Medical electrical impedance tomography systems prescribe electrode currents. Accurate voltages, even on passive electrodes, require the complete electrode model (CEM), the most accurate model known for skin electrodes. Its current constraints specify total electrode currents, whereas standard finite element boundary interfaces impose local conditions. We describe how to implement the CEM through Robin boundary conditions, which are available in standard FEM packages. Approach. Prescribed electrode potentials give Robin problems whose unit-potential responses define an electrode transadmittance matrix. We prove equivalence to the conventional augmented CEM and formalise the discrete energy, equivalence and reference arguments in Lean 4. Numerical comparisons use two-dimensional P1 finite elements in DOLFINx, FreeFEM, GetFEM, MFEM and NGSolve, with the same nodal reference potential as EIDORS. Main results. For identical assembled blocks, the two procedures have the same solution set whenever the Robin matrix is invertible. Across the five packages in dimensionless test cases, maximum absolute electrode-potential differences from EIDORS were below $5\times10^{-13}$ (2692 nodes), and errors against an exact rational-arithmetic reference were below $1.1\times10^{-13}$ (2535 nodes). Significance. Standard Robin solves and consistent electrode-current integration provide a portable route to the current-driven CEM, with current constraints enforced through a small electrode system.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bing-Zhou Chen, Amelia Deakin, Andy Adler, Paul D. Ledger, William R. B. Lionheart. 2026-10-16. Implementing the EIT complete electrode model through Robin boundary conditions. https://arxiv.org/abs/2610.22944

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Effect of Right Ventricular Outflow Tract Material Properties on Simulated Transcatheter Pulmonary Placement

FE simulations emulating TPV system deployment in patient-specific right ventricular outflow tracts (RVOT) assume material properties for the RVOT and adjacent tissues. Sensitivity of the deployment to variation in RVOT material properties is unknown. Moreover, the effect of a transannular patch stiffness and location on simulated TPV deployment has not been explored. A sensitivity analysis on the material properties of a patient-specific RVOT during TPV deployment, modeled as an uncoupled HGO material, was conducted using FEBioUncertainSCI. Further, the effects of a transannular patch during TPV deployment were analyzed by considering two patch locations and four patch stiffnesses. Visualization of results and quantification were performed using custom metrics implemented in SlicerHeart and FEBio. Sensitivity analysis revealed that the shear modulus of the ground matrix (c), fiber modulus (k1), fiber dispersion (kappa), and fiber mean orientation angle (gamma) had the greatest effect on 95th%ile stress, whereas only c had the greatest effect on 95th%ile Lagrangian strain. First-order sensitivity indices contributed the greatest to the total-order sensitivity indices. Simulations using a transannular patch revealed that peak stress and strain were dependent on patch location and the underlying vessel geometry, with increasing patch stiffness modulating the magnitude of local stress concentrations. As stiffness of the patch increased, greater stress was observed at the interface connecting the patch to the RVOT, and stress in the patch itself increased while strain decreased. The total enclosed volume by the TPV device remained unchanged across all simulated patch cases. This study highlights that while uncertainties in tissue material properties and patch locations may influence functional outcomes, FE simulations provide a reliable framework for evaluating these outcomes in TPVR.

physics.med-ph↗

Angle-domain null subtraction imaging from beamformed plane-wave data

Null subtraction imaging (NSI) narrows the apparent lateral response by combining fields produced with a zero-mean apodization and two DC-offset variants. Usually, NSI requires channel data and flexible beamformers (Receive-NSI). We investigate Angle-NSI, which operates on per-angle images during coherent plane-wave compounding. A matrix formulation was used to distinguish receive-domain and angle-domain null subtraction expressions. The method was evaluated using a simulated point target, experimental resolution phantom targets, an open microbubble (MB) traces dataset and two in vivo carotid acquisitions. Angle sampling, noise, phase jitter and target position were swept in simulation. A synchronized GPU benchmark compared the complete reconstructions. Six beamforming methods were compared: delay-and-sum (DAS), coherence-factor-weighted DAS (CF-DAS), minimum variance beamforming (MV), delay-multiply-and-sum (DMAS), Receive-NSI and Angle-NSI. Angle-NSI reduced the simulated -6 dB lateral width from 0.226 mm for DAS to 0.116 mm, compared with 0.009 mm for Receive-NSI, while the axial width was 0.720 mm for these three methods. The lateral narrowing was confirmed on an experimental resolution phantom. Angle-NSI had shorter reconstruction time than Receive-NSI and the lowest post-delay processing time among the nonlinear methods. In power Doppler, microbubble trace widths were reduced by 50.2% for Receive-NSI and 30.9% for Angle-NSI compared to DAS. On a phantom and in vivo, both NSI variants reduced generalized contrast-to-noise ratio relative to the conventional methods. Angle-NSI provides simple post-processing of per-angle beamformed images. It narrows the apparent lateral response at low computational cost, at the expense of B-mode detectability: this makes it a well-suited alternative for high-resolution power Doppler applications.

physics.med-ph↗

PMB-NN: Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes

Accurate, physiologically consistent glucose forecasts could help people with type 1 diabetes respond to impending excursions. We applied our physiological-model-based neural network (PMB--NN) framework to glucose forecasting. A temporal convolutional network (TCN) used 180 min of history, constrained during training by a personalized four-parameter physiological model. The main PMB--NN combined forecast and neural--physiological agreement losses; a three-term variant additionally fitted physiological predictions to measured glucose. Data comprised 6--8 weeks of continuous glucose monitoring, pump, and meal records from 25 AZT1D participants. Person-specific models used chronological 70/10/20 train/validation/test splits and 30-, 60-, and 120-min horizons. Median mean absolute errors were 16.24, 25.00, and 30.20 mg/dL with basal insulin records and 15.38, 24.21, and 31.79 mg/dL before these records became available. In the latter setting, PMB--NN had the lowest median error at 30 and 60 min; it outperformed the physiological model alone in all six settings. In input-increase tests, glucose predictions changed in the expected direction for 85\% of correction boluses and 100\% of carbohydrate rescues, versus 57\% for active insulin and 56\% for active carbohydrates. Most standalone physiological fits showed locally distinguishable parameter effects, with glucose-related coefficients more stable under resampling than insulin- or carbohydrate-related coefficients. The three-term variant improved hyperglycaemia accuracy with basal records, but did not significantly change overall MAE and reduced directional plausibility in three tests. PMB--NN combines personalized forecasting with an explicit physiological model, allowing assessment of predictive accuracy, physiological response behavior, and participant-specific coefficients.

physics.med-ph↗