Evaluation of referenceless thermometry in MRI‐guided focused ultrasound surgery of uterine fibroids
Bibliographic record
Abstract
PURPOSE: To clinically assess a previously described method (Rieke et.al., Magn Reson Med 2004) to produce more motion-robust MRI-based temperature images using data acquired during MRI-guided focused ultrasound surgery (MRgFUS) of uterine fibroids. MATERIALS AND METHODS: The method ("referenceless thermometry") uses surface fitting in nonheated regions of individual phase images to extrapolate and then remove background phase variations that are unrelated to temperature changes. We tested this method using images from 100 sonications selected from 33 patient MRgFUS treatments. Temperature measurements and thermal dose contours estimated with the referenceless method were compared with those produced with the standard phase-difference technique. Fitting accuracy and noise level were also measured. RESULTS: In 92/100 sonications, the difference between the two measurements was less than 3 degrees C. The average difference in the measurements was 1.5 +/- 1.4 degrees C. Small motion artifacts were observed in the phase-difference imaging when the difference was greater than 3 degrees C. The method failed in two cases. The mean absolute error in the surface fit in baseline images corresponded to a temperature error of 0.8 +/- 1.4 degrees C. The noise level was approximately 40% lower than the phase-difference method. Thermal dose contours calculated from the two methods agreed well on average. CONCLUSION: Based on the small error when compared with the standard technique, this method appears to be adequate for temperature monitoring of MRgFUS in uterine fibroids and may prove useful for monitoring temperature changes in moving organs.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".