Improved Configurations for 3D Acoustoelectric Tomography With a Minimal Number of Electrodes
Bibliographic record
Abstract
OBJECTIVE: Acoustoelectric tomography (AET) is a hybrid imaging technique combining ultrasound and electrical impedance tomography (EIT). It exploits the acoustoelectric effect (AAE): an US wave propagating through the medium induces a local change in conductivity, depending on the acoustoelectric properties of the medium. Typically, AET image reconstruction is limited to 2D and most cases employ a large number of surface electrodes. METHODS: This article investigates the detectability of contrasts in AET. We characterize the AEE signal as a function of the medium conductivity and electrode placement, using a novel 3D analytical model of the AET forward problem. The proposed model is compared to a finite element method simulation. RESULTS: In a cylindrical geometry with an inclusion contrast of 5 times the background and two pairs of electrodes, the maximum, minimum, and mean suppression of the AEE signal are 68.5%, 3.12%, and 49.0%, respectively, over a random scan of electrode positions. The proposed model is compared to a finite element method simulation and the minimum mesh sizes required successfully model the signal is estimated. CONCLUSION: We show that the coupling of AAE and EIT leads to a suppressed signal and the magnitude of the reduction is a function of geometry of the medium, contrast and electrode locations. SIGNIFICANCE: This model can aid in the reconstruction of AET images involving a minimum number of electrodes to determine the optimal electrode placement.
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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 | 0.001 | 0.003 |
| 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".