Electrical impedance tomography in 3D using two electrode planes: characterization and evaluation
Bibliographic record
Abstract
Electrical impedance tomography (EIT) uses body surface electrical stimulation and measurements to create conductivity images; it shows promise as a non-invasive technology to monitor the distribution of lung ventilation. Most applications of EIT have placed electrodes in a 2D ring around the thorax, and thus produced 2D cross-sectional images. These images are unable to distinguish out-of-plane contributions, or to image volumetric effects. Volumetric EIT can be calculated using multiple electrode planes and a 3D reconstruction algorithm. However, while 3D reconstruction algorithms are available, little has been done to understand the performance of 3D EIT in terms of the measurement configurations available. The goal of this paper is to characterize the phantom and in vivo performance of 3D EIT with two electrode planes. First, phantom measurements are used to measure the reconstruction characteristics of seven stimulation and measurement configurations. Measurements were then performed on eight healthy volunteers as a function of body posture, postures, and with various electrode configurations. Phantom results indicate that 3D EIT using two rings of electrodes provides reasonable resolution in the electrode plane but low vertical resolution. For volunteers, functional EIT images are created from inhalation curve features to analyze the effect of posture (standing, sitting, supine and decline) on regional lung behaviour. An ability to detect vertical changes in lung volume distribution was shown for two electrode configurations. Based on tank and volunteer results, we recommend the use of the 'square' stimulation and measurement pattern for two electrode plane EIT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".