Imaging Electrical Impedance From Acoustic Measurements by Means of Magnetoacoustic Tomography With Magnetic Induction (MAT-MI)
Bibliographic record
Abstract
We have conducted computer simulation and experimental studies on magnetoacoustic-tomography with magnetic induction (MAT-MI) for electrical impedance imaging. In MAT-MI, the object to be imaged is placed in a static magnetic field, while pulsed magnetic stimulation is applied in order to induce eddy current in the object. In the static magnetic field, the Lorentz force acts upon the eddy current and causes acoustic vibrations in the object. The propagated acoustic wave is then measured around the object to reconstruct the electrical impedance distribution. In the present simulation study, a two-layer spherical model is used. Parameters of the model such as sample size, conductivity values, strength of the static and pulsed magnetic field, are set to simulate features of biological tissue samples and feasible experimental constraints. In the forward simulation, the electrical potential and current density are solved using Poisson's equation, and the acoustic pressure is calculated as the forward solution. The electrical impedance distribution is then reconstructed from the simulated pressure distribution surrounding the sample. The present computer simulation results suggest that MAT-MI can reconstruct conductivity images of biological tissue with high spatial resolution and high contrast. The feasibility of MAT-MI in providing high spatial resolution images containing impedance-related information has also been demonstrated in a phantom experiment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".