Effect of acoustic impedance mismatch between skin and bone on transcranial ultrasound transmission
Bibliographic record
Abstract
A major problem with transcranial Doppler (TCD) ultrasound is the poor transmission of ultrasound through the skull bone causing image quality degradation. The reasons for the poor image quality are (1) acoustic impedance mismatch along the wave propagation path and (2) bone frequency-dependent attenuation. Transmission loss due to acoustic impedance mismatch is typically ignored in the literature. Therefore, the objective of this paper is to study the effect of acoustic impedance mismatch on the transmitted energy as a function of frequency. To achieve this, the wave propagation was modelled analytically from the ultrasonic transducer into the brain. The model calculates frequency-dependent transmission coefficient for a given skin and bone thickness combination. The model incorporates both attenuation and acoustic impedance mismatch effects. The model was validated experimentally by comparing with measurements using a bone phantom plate mimicking the acoustic properties of the skull. The average error on the skin thickness between the model and the experiment was less than 6% for a constant bone thickness. Further analysis of the simulation suggests that an optimized excitation frequency can be chosen based on skin and bone thicknesses that would improve ultrasound transmission.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".