Electrical impedance computed tomography in three-dimensional imaging
Bibliographic record
Abstract
Electrical impedance computed tomography (EICT) is the technique which reconstructs the image within a body based on the variation of electrical conductivity and permittivity by using electrical measurements on its boundary. It has the advantages of reduced biological hazard and less expensive hardware, therefore, it can be used as an alternative means for continuous monitoring in medical or environmental applications. Efforts have been made to develop algorithms and to improve the image quality in 2D imaging. Many algorithms employ the Poisson (Laplace) equation to describe the potential distributions with the finite element method. The present paper discusses the models for EICT in 3D applications and the excitation/measurement pattern effects on image quality, and presents simulation results. A network approach to study the features of EICT algorithms is introduced in the paper, so that the discretization problems will not affect the evaluations of an EICT algorithm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".