A viscoelastic model for the prediction of transcranial ultrasound propagation: application for the estimation of shear acoustic properties in the human skull
Bibliographic record
Abstract
A better understanding of ultrasound transmission through the human skull is fundamental to develop optimal imaging and therapeutic applications. In this study, we present global attenuation values and functions that correlate apparent density calculated from computed tomography scans to shear speed of sound. For this purpose, we used a model for sound propagation based on the viscoelastic wave equation (VWE) assuming isotropic conditions. The model was validated using a series of measurements with plates of different plastic materials and angles of incidence of 0°, 15° and 50°. The optimal functions for transcranial ultrasound propagation were established using the VWE, scan measurements of transcranial propagation with an angle of incidence of 40° and a genetic optimization algorithm. Ten (10) locations over three (3) skulls were used for ultrasound frequencies of 270 kHz and 836 kHz. Results with plastic materials demonstrated that the viscoelastic modeling predicted both longitudinal and shear propagation with an average (±s.d.) error of 9(±7)% of the wavelength in the predicted delay and an error of 6.7(±5)% in the estimation of transmitted power. Using the new optimal functions of speed of sound and global attenuation for the human skull, the proposed model predicted the transcranial ultrasound transmission for a frequency of 270 kHz with an expected error in the predicted delay of 5(±2.7)% of the wavelength. The sound propagation model predicted accurately the sound propagation regardless of either shear or longitudinal sound transmission dominated. For 836 kHz, the model predicted accurately in average with an error in the predicted delay of 17(±16)% of the wavelength. Results indicated the importance of the specificity of the information at a voxel level to better understand ultrasound transmission through the skull. These results and new model will be very valuable tools for the future development of transcranial applications of ultrasound therapy and imaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".