Safely assessing radiofrequency heating potential of conductive devices using image‐based current measurements
Bibliographic record
Abstract
PURPOSE: Many procedures involving catheters and implanted medical devices could benefit from MRI guidance but are currently contraindicated due to risk of significant heating near linear conductive structures. A priori safety prediction is impossible in vivo and thus, safety is typically investigated in vitro by directly measuring temperature rise. Existing methods of investigating safety are inflexible and provide few data. Furthermore, they are fundamentally limited because dangerous temperatures rises can only be investigated if induced. A method of remotely predicting safety is necessary for ensuring safety in patients. THEORY AND METHODS: Electric current induced on the metallic object causes any dangerous heating; thus a remote method of safely characterizing the induced radiofrequency (RF) current distribution would suffice to evaluate safety assuming conservative estimates for local tissue properties. Here we propose a method of analyzing induced phase artifacts seen in low-specific absorption rate characterization images, to determine induced current on an interventional device. This induced current distribution can then be used to predict RF heating behavior under application of any other imaging sequence. RESULTS: This method has been successfully used to reproduce numerical simulations in a phantom. Furthermore, the heating behavior around a conductive wire produced by a scan other than that used to characterize current was successfully predicted. CONCLUSION: It has been shown in phantom experiments that remote current characterization can safely prevent dangerous scans as well as enable safe scans that previously would not have been attempted.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".