Modeling wave propagation through the skull for ultrasonic transcranial Doppler
Bibliographic record
Abstract
One problem associated with transcranial Doppler ultrasound (TCD) is the relatively low energy penetrating inside the brain through the skull, seriously limiting the image quality. This may be due to the impedance mismatch at the bone interface and to the bone frequency dependent attenuation. The objective of this paper is to model ultrasonic wave propagation through the skull. To do so, an analytical model was developed based on the estimation of the transmission coefficients inside the brain, leading to frequency dependent overall transmission coefficient for a given skin and bone thickness. Moreover, a finite element model was developed taking into account absorption phenomena. Both methods were validated experimentally by comparing the numerical and analytical results with results obtained from a phantom mimicking the skull having an attenuation coefficient equal to 30 dB/cm at 2.25 MHz and a thickness of 4.4 mm. A 2 mm layer of water mimicked the skin. The difference between the maximum amplitude of normalized received US signals obtained analytically and experimentally was 1%. The average relative difference between them was 0.3%. Thus, a working model is designed which can predict the energy inside the brain. Furthermore, the results show that impedance mismatch plays a major role in transmission loss rather than frequency dependent attenuation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".