A Study Of The Acoustic Profile Of A Segmented Clinical Focused Ultrasound Transducer
Bibliographic record
Abstract
The use of focused ultrasound as a minimally invasive treatment for tumours is expanding in the UK. The main target organs are the liver and kidneys. Single element and phased array transducers may be used clinically. In this paper some of the possibilities available with a linear array transducer in which all elements are driven in phase are investigated. An acoustic field program based on the optical Fresnel‐Kirchhoff diffraction theory has been used to model a segmented transducer. This device is a 15 cm focal length spherical bowl with an aperture diameter of 11 cm and a 5 cm diameter central hole in which may be placed an ultrasound imaging probe. It operates at 1.7 MHz and consists of ten equal area parallel strips. The aim of this study was to investigate how, by removing the contribution of individual segments to the field, a useful beam profile can be achieved using a combination of different active segments. An example of its possible use is to spare the ribs from potentially harmful intensities when treating liver tumours. Simulations of the acoustic field at the focus and in the area of the beam where ribs might be present were performed. The profile of the beam is compared for a number of combinations of active segments. It is shown that it is possible, by appropriate switching of the segments, to significantly reduce the intensity at the rib surface.
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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 | 0.000 | 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".