Numerical simulation of transcranial focused ultrasound therapy
Bibliographic record
Abstract
Transcranial focused ultrasound therapy is becoming a viable tool for treatment of brain related disorders. The technique has been used for treatment of essential tremors and chronic neuropathic pain with good results. In addition, the technique has a broad scope of potential applications including: tumor ablation, localized drug delivery, thrombolysis, and neurostimulation. Performing an efficient focused ultrasound treatment delivery requires planning. Accurate computational models could, in principle, be utilized to provide auxiliary guidelines for such planning. In this work, a computational model is presented for simulating the propagation of ultrasound in transcranial setting. The effects of ultrasound propagation through soft tissue and bone are modeled by coupling wave equations of fluid and solid. The coupled model is numerically evaluated by using a hybrid simulation technique that couples finite difference method with a grid method. The resulting computational model is utilized to simulate focused ultrasound field in clinical patient treatment setting. Effect of absorption of heat into the brain tissue is further simulated by using the bioheat equation. Simulation results are compared with magnetic resonance thermometry performed during the clinical treatments for evaluation of validity of the computational model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".