Temperature‐dependent MR signals in cortical bone: Potential for monitoring temperature changes during high‐intensity focused ultrasound treatment in bone
Bibliographic record
Abstract
PURPOSE: Because existing magnetic resonance thermometry techniques do not provide temperature information within bone, high-intensity focused ultrasound (HIFU) exposures in bone are monitored using temperature changes in adjacent soft tissues. In this study, the potential to monitor temperature changes in cortical bone using a short TE gradient echo sequence is evaluated. METHODS: The feasibility of this proposed method was initially evaluated by measuring the temperature dependence of the gradient echo signal during cooling of cortical bone samples implanted with fiber-optic temperature sensors. A subsequent experiment involved heating a cortical bone sample using a clinical MR-HIFU system. RESULTS: A consistent relationship between temperature change and the change in magnitude signal was observed within and between cortical bone samples. For the two-dimensional gradient echo sequence implemented in this study, a least-squares linear fit determined the percentage change in signal to be (0.90 ± 0.01)%/°C. This relationship was used to estimate temperature changes observed in the HIFU experiment and these temperatures agreed well with those measured from an implanted fiber-optic sensor. CONCLUSION: This method appears capable of displaying changes related to temperature in cortical bone and could improve the safety of MR-HIFU treatments. Further investigations into the sensitivity of the technique in vivo are warranted.
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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) | 0.001 | 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".