Microbubble-enhanced transcranial MR-guided focused ultrasound brain hyperthermia: heating mechanism investigation using finite element method
Bibliographic record
Abstract
Recently, our group developed a synergistic brain drug delivery method to achieve simultaneous transcranial hyperthermia and localized blood-brain barrier opening via MR-guided focused ultrasound (MRgFUS). In a rodent model, we demonstrated that the ultrasound power required for transcranial MRgFUS hyperthermia was significantly reduced by injecting microbubbles (MBs). However, the specific mechanisms underlying the power reduction caused by MBs remain unclear. The present study aims to elucidate the mechanisms of MB-enhanced transcranial MRgFUS hyperthermia through numerical studies using the finite element method. The microbubble acoustic emission (MAE) and the viscous dissipation (VD) were hypothesized to be the specific mechanisms. Acoustic wave propagation was used to model the FUS propagation in the brain tissue, and a bubble dynamics equation for describing the dynamics of MBs with small shell thickness was used to model the MB oscillation under FUS exposures. A modified bioheat transfer equation was used to model the temperature in the rodent brain with different heat sources. A theoretical model was used to estimate the bubble shell's surface tension, elasticity, and viscosity losses. The simulation reveals that MAE and VD caused a 40.5% and 52.3% additional temperature rise, respectively. Compared with FUS only, MBs caused a 64.0% temperature increase, which is consistent with our previous animal experiments. Our investigation showed that MAE and VD are the main mechanisms of MB-enhanced transcranial MRgFUS hyperthermia.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".