Characterization of magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU)-induced large-volume hyperthermia in deep and superficial targets in a porcine model
Bibliographic record
Abstract
Purpose To characterize temperature fields and tissue damage profiles of large-volume hyperthermia (HT) induced by magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) in deep and superficial targets in vivo in a porcine model.Methods Nineteen HT sessions were performed in vivo with a commercial MRgHIFU system (Sonalleve® V2, Profound Medical Inc., Mississauga, ON, Canada) in hind leg muscles of eight pigs with temperature fields of cross-sectional diameter of 58-mm. Temperature statistics evaluated in the target region-of-interest (tROI) included accuracy, temporal variation, and uniformity. The impact of the number and location of imaging planes for feedback-based temperature control were investigated. Temperature fields were characterized by time-in-range (TIR, the duration each voxel stays within 40–45 °C) maps. Tissue damage was characterized by contrast-enhanced MRI, and macroscopic and histopathological analysis. The performance of the Sonalleve® system was benchmarked against a commercial phantom.Results Across all HT sessions, the mean difference between the average temperature (Tavg) and the desired temperature was −0.4 ± 0.5 °C; the standard deviation of temperature 1.2 ± 0.2 °C; the temporal variation of Tavg for 30-min HT was 0.6 ± 0.2 °C, and the temperature uniformity was 1.5 ± 0.2 °C. A difference of 2.2-cm (in pig) and 1.5-cm (in phantom) in TIR dimensions was observed when applying feedback-based plane(s) at different locations. Histopathology showed 62.5% of examined HT sessions presenting myofiber degeneration/necrosis within the target volume.Conclusion Large-volume MRgHIFU-mediated HT was successfully implemented and characterized in a porcine model in deep and superficial targets in vivo with heating distributions modifiable by user-definable parameters.
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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".