2D noninvasive acoustical image reconstruction of a static object through a simulated human skull bone
Bibliographic record
Abstract
A new method for 2D visualization of foreign objects in the brain tissue, such as bone fragments, bullets, pieces of shrapnel, etc. is presented. The method uses acoustic ray tracing approach to model the propagation of ultrasonic waves through the skull bone and the brain tissue. The mathematical theory of the method, the preliminary results of computer modeling and laboratory testing are presented. A simulation has been developed to take into account the scattering of acoustical fields transmitted through a human skull bone. The experimental data is processed and an image showing the position of the foreign object is reconstructed. The new algorithm has been designed to work with a linear array of 128 receivers. The model consists of a simulated skull bone (scattering medium) and a reflector as a secondary source of ultrasound. To experimentally check the validity of the algorithm, a skull phantom was prepared for use in the laboratory tests. After passing through the phantom layer, the secondary ultrasound field originated from the reflector is recorded by the array of receivers. Then, the detected field distribution is signal-processed to compensate for the distortion by the scattering layer and to reconstruct an image containing data about the reflector's position. This method opens the possibility to non-invasively visualize and characterize the inclusions in the brain tissue through the skull.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".