Development of a practical ultrasonic approach for simultaneous measurement of the thickness and the sound speed in human skull bones: a laboratory phantom study
Bibliographic record
Abstract
The availability of a non-invasive express method for the in vivo measurement of both sound velocity and thickness of the human skull bone would be of great benefit to various transcranial ultrasonic imaging and treatment applications. This paper investigates two ultrasonic methods that measure both parameters and are based on the variable focus technique. All the experiments described in this paper were conducted on specially prepared custom skull bone phantoms, including flat and deformed samples, designed and developed in our laboratory. The first method uses a single immersion 2.25 MHz ultrasonic transducer consecutively focused on the front and back surfaces of the sample. The accuracy and precision of this method are demonstrated via single point measurements on flat samples with and without porosity. The measurement results from a specimen with the randomly curved back surface show the possibility of obtaining the inner profile of the skull bone. The second presented method is a practical modification of the variable focus technique for the linear phased array case. The method was tested on flat and curved skull bone phantoms with and without inner porosity showing higher measurement accuracy and simpler practical realization than its scanning counterpart.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".