Comparison of Applied and Induced Current Electrical Impedance Tomography
Bibliographic record
Abstract
Several papers on induced current electrical impedance tomography (IC-EIT) have dwelt on potential advantages of this technique over conventional EIT which uses applied current (AC-EIT). Experimental evidence that IC-EIT could surpass AC-EIT in similar imaging conditions is lacking. In this paper, we describe a system that can switch rapidly between both AC-EIT and IC-EIT. The system makes it possible to image objects in a saline-filled tank, providing data acquired in identical test conditions for comparing the performance of the two modes. The system uses eight circular coils and 16 electrodes to acquire 120 linearly independent measurements in IC-EIT and 104 in AC-EIT. Difference images were reconstructed from data acquired with both modes using the maximum a posteriori method. Spatial resolution was lower in IC-EIT images than in AC-EIT, especially in the radial direction. IC-EIT also exhibits a bias toward the center for positioning a conductivity perturbation. These results were obtained for a typical coil configuration widely used in the literature and may not be representative of alternate coil configurations. The system described in this paper provides stable experimental conditions for comparing the performance of the two EIT imaging modes and would be a valuable tool for validating new coil configurations.
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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 | 0.001 | 0.000 |
| 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".