An experimental study on the effect of temperature on the acoustic properties of cranial bone
Bibliographic record
Abstract
Transcranial focused ultrasound is increasingly being used as an alternative non-invasive treatment for various brain disorders, including essential tremor, Parkinson's disease, and neuropathic pain. These applications necessitate an understanding of the complex relationship between temperature and acoustic properties of cranial bone. In particular, the longitudinal speed of sound and attenuation coefficients will be investigated. In this study, ex vivo skull caps were heated to temperatures ranging from 20 to 50 °C, and ultrasound pulses were transmitted through the skull caps using a spherical transducer of 5 cm diameter and 10 cm focal length, at clinically relevant frequencies of 0.836 and 1.402 MHz. A thin Mylar film was placed at the focus, and a laser vibrometer was used to receive the ultrasound pulse transmitted through the skull. It was found that there was a measurable change in the phase and amplitude of the received signal, implying a change in both the speed of sound and attenuation of the bone at different temperatures. It was also found that these changes were completely reversible. These results imply that at sufficiently high cranial bone temperatures, the assumption of temperature-independent acoustic properties of bone may become invalid.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".