Numerical simulations of clinical focused ultrasound functional neurosurgery
Bibliographic record
Abstract
A computational model utilizing grid and finite difference methods were developed to simulate focused ultrasound functional neurosurgery interventions. The model couples the propagation of ultrasound in fluids (soft tissues) and solids (skull) with acoustic and visco-elastic wave equations. The computational model was applied to simulate clinical focused ultrasound functional neurosurgery treatments performed in patients suffering from therapy resistant chronic neuropathic pain. Datasets of five patients were used to derive the treatment geometry. Eight sonications performed in the treatments were then simulated with the developed model. Computations were performed by driving the simulated phased array ultrasound transducer with the acoustic parameters used in the treatments. Resulting focal temperatures and size of the thermal foci were compared quantitatively, in addition to qualitative inspection of the simulated pressure and temperature fields. This study found that the computational model and the simulation parameters predicted an average of 24 ± 13% lower focal temperature elevations than observed in the treatments. The size of the simulated thermal focus was found to be 40 ± 13% smaller in the anterior-posterior direction and 22 ± 14% smaller in the inferior-superior direction than in the treatments. The location of the simulated thermal focus was off from the prescribed target by 0.3 ± 0.1 mm, while the peak focal temperature elevation observed in the measurements was off by 1.6 ± 0.6 mm. Although the results of the simulations suggest that there could be some inaccuracies in either the tissue parameters used, or in the simulation methods, the simulations were able to predict the focal spot locations and temperature elevations adequately for initial treatment planning performed to assess, for example, the feasibility of sonication. The accuracy of the simulations could be improved if more precise ultrasound tissue properties (especially of the skull bone) could be obtained.
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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 | 0.000 | 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".