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 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 | 0.002 |
| 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".