Toward in vivo quantification of induced RF currents on long thin conductors
Bibliographic record
Abstract
PURPOSE: Most MR-guided catheter-based procedures, and imaging of patients with implanted medical devices, are currently contraindicated due to a significant risk of heating associated with induced RF currents. The induced RF current produces a corresponding artifact which can be used to remotely characterize current and safely predict RF heating. Application of this remote technique in vivo to safely quantify RF heating risk may allow for execution of many scans currently contraindicated. Sources of phase other than induced RF current may present difficulty in practical in vivo. METHODS: A custom ultra-short echo time (UTE) sequence was developed to minimize unwanted phase contributions. A phantom experiment was performed to compare current characterization using a stock gradient-echo (GRE) sequence and the custom UTE sequence following calibration of the temperature measurement apparatus using a previously published heating prediction technique. Animal experiments were used to investigate the feasibility of using the UTE sequence to quantify RF heating. RESULTS: Current characterization and heating prediction with a stock GRE sequence was equivalent to that with the custom UTE sequence. Heating measurements and image-based predictions in animal experiments agreed within error in all experiments. CONCLUSION: Through comparison of measured heating and image-based prediction, feasibility of using a custom UTE sequence to quantify RF heating risk in vivo was demonstrated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".