Transducer module apodization for reducing bone heating during focused ultrasound uterine fibroid ablation
Bibliographic record
Abstract
During MR-guided focused ultrasound (MRgFUS) for uterine fibroids, thermoablation of tissue near spine/hips is challenging due to bone heating that can cause patient pain and potentially damage nerves. Here we investigate transducer module apodization for maximizing the focal-to-bone heating ratio (ΔT ;ratio) in silico using a 6144-element flat fully populated phased array operating at 0.5 MHz (Arrayus Technologies, Inc.). Acoustic and thermal simulations were performed using anatomies of ten patients who underwent MRgFUS ablation for uterine fibroids with this device as part of a clinical trial (NCT03323905). Transducer modules (64 elements/module) whose beams intersected no-pass regions were identified, their amplitudes were reduced by varying blocking percentage levels, and the resulting temperature field distributions were evaluated across multiple sonications per patient. For all simulated sonications transducer module blocking improved ΔT ;ratio compared to no blocking. In 42% of sonications, full module blocking maximized ΔT ;ratio, with mean improvements of 97% ± 55% and 47% ± 36% in hip and spine compared to no blocking, at the cost of increased focal thermal volumes and acoustic power levels. In the remaining sonications, partial module blocking provided increased ΔT ;ratio values (39% ± 45% in hip, 19% ± 15% in spine targets) relative to full blocking. The optimal blocking percentage varied depending on the specific treatment geometry.
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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 | 0.001 |
| 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) | 0.001 | 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".