Current density imaging of electrical current pathways inside the pig torso
Bibliographic record
Abstract
Low frequency current density imaging (LFCDI) using a magnetic resonance (MR) imager has been shown to accurately measure electrical current density inside a phantom. CDI measures the magnetic field generated by the current and converts it to current density (CD) by computing its curl. Therefore, CDI avoids both the inverse problem and invasiveness of other electrical measurement techniques such as electrical impedance tomography and direct electrode measurement. This makes CDI an ideal technique for studying the current flow inside the body during electrical therapies such as defibrillation where the current density in tissue is closely associated with the efficacy. Here we report simultaneous measurements of current density at all points within the pig torso during an electrical current application through defibrillation electrodes. Current flow was visualized by computing streamlines from the current density vectors. We observed current flow over the chest walls in agreement with the current literature. However, complex and unexpected current flow patterns were seen inside the heart as well as in the surrounding vasculature. This study represents the first noninvasive volume current measurement inside the pig torso during an electrical current application.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".