Drift correction for accurate PRF-shift MR thermometry during mild hyperthermia treatments with MR-HIFU
Bibliographic record
Abstract
UNLABELLED: There is growing interest in performing hyperthermia treatments with clinical magnetic resonance imaging-guided high-intensity focused ultrasound (MR-HIFU) therapy systems designed for tissue ablation. During hyperthermia treatment, however, due to the narrow therapeutic window (41-45 °C), careful evaluation of the accuracy of proton resonant frequency (PRF) shift MR thermometry for these types of exposures is required. PURPOSE: The purpose of this study was to evaluate the accuracy of MR thermometry using a clinical MR-HIFU system equipped with a hyperthermia treatment algorithm. METHODS: Mild heating was performed in a tissue-mimicking phantom with implanted temperature sensors using the clinical MR-HIFU system. The influence of image-acquisition settings and post-acquisition correction algorithms on the accuracy of temperature measurements was investigated. The ability to achieve uniform heating for up to 40 min was evaluated in rabbit experiments. RESULTS: Automatic centre-frequency adjustments prior to image-acquisition corrected the image-shifts in the order of 0.1 mm/min. Zero- and first-order phase variations were observed over time, supporting the use of a combined drift correction algorithm. The temperature accuracy achieved using both centre-frequency adjustment and the combined drift correction algorithm was 0.57° ± 0.58 °C in the heated region and 0.54° ± 0.42 °C in the unheated region. CONCLUSION: Accurate temperature monitoring of hyperthermia exposures using PRF shift MR thermometry is possible through careful implementation of image-acquisition settings and drift correction algorithms. For the evaluated clinical MR-HIFU system, centre-frequency adjustment eliminated image shifts, and a combined drift correction algorithm achieved temperature measurements with an acceptable accuracy for monitoring and controlling hyperthermia exposures.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".