The utility of sparse 2D fully electronically steerable focused ultrasound phased arrays for thermal surgery: a simulation study
Bibliographic record
Abstract
Sparse arrays are widely used in diagnostic ultrasound for their strong performance and relative technical simplicity. This simulation study assessed the efficacy of phased arrays of varied sparseness for thermal surgery, especially with regard to power consumption and near-field heating. It employs a linear ultrasound propagation model and a semi-analytical solution to the Pennes' bioheat transfer equation. The basic design had 4912 cylindrical transducers (500 kHz) arranged on a flat 12 cm disk (1.5 mm spacing). This array was compared to randomly-thinned sparse arrays with 75%, 50% and 25% populations. Temperature elevations of 60 and 70 °C were induced in sonication times of 5-20 s, at foci spanning depths of 50-150 mm and radii of 0-60 mm. The sparse arrays produced nearly indistinguishable focal patterns but, averaged across the foci, required 132%, 200% and 393% of the power of the full array, respectively, applied through fewer transducer elements. Comparable results were found at 1 MHz from equivalent arrays. Simulated lesions were formed (thermal dose ⩾ 240 equivalent minutes at 43 °C (T(43))) and 'transition' and 'unsafe' regions (both defined as 5 min < T(43) < 240 min) were identified, the former immediately surrounding the lesion and the latter anywhere else. At a depth of 100 mm, sparse arrays were found to produce comparable lesions to the full array at the focus, but 'unsafe', over-heated near-field regions after some ablated lesion volume: about 12 mL for the 25% array, around 100 mL for the 50% array, while the 75% and full arrays produced 150 mL lesions safely.
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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 | 0.001 | 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".