Theoretical evaluation of moderately focused spherical transducers and multi-focus acoustic lens/transducer systems for ultrasound thermal therapy
Bibliographic record
Abstract
Impractically long treatment times are required for highly focused spherical transducers to destroy large tumours because thermal lesions generated by these transducers are small and a large number of such lesions are required. Moderately focused spherical transducers and multi-focus acoustic lens/transducer systems can generate larger thermal lesions compared to those produced by highly focused spherical transducers, and therefore shorter treatment times can be expected. The decrease in total treatment time by the use of moderately focused spherical transducers and acoustic lens/transducer systems was quantified in this study. A 3D ultrasound thermal model was developed to predict thermal lesion volumes generated by ultrasound transducers. A target model was constructed in order to compare various transducer designs under identical treatment conditions and with identical treatment goals. A design method was developed to determine the thickness of lens elements for production of specified multi-focus fields. Then, a highly focused and a moderately focused spherical transducer, and an acoustic lens/transducer system were compared in terms of total time required to treat a tumour. These transducers had identical apertures and operating frequencies. The radius of curvature of the moderately focused spherical transducer was chosen such that the length of thermal lesions it generated over 10 s single exposures was slightly greater than that of the target. The lens/transducer system was designed to produce a 9 focus field. The simulation results show that for the treatment of a 2 x 2 x 2 cm3 tumour at a depth of 5 cm in the body, the highly focused spherical transducer, the moderately focused spherical transducer and the lens/transducer system required 150, 42 and 30 min, respectively.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".