Computational evidence for a discrete-scatterer aberration model in medical ultrasound
Bibliographic record
Abstract
Many techniques for correcting ultrasound focus distortion model the aberrating properties of tissue with a single time-shift screen, but simulations and phantom studies suggest single-screen models are ineffective for transmit focus compensation. Extension of the models to include multiple parallel screens is a logical increment in complexity, but the number of screens must be manageable and readily determined to yield practical aberration correction methods. To assess the feasibility of multi-screen strategies, simulations were performed to search for a general form for the aberration profile of breast tissue. Two-dimensional propagation of 3-MHz planar wavefronts through digitized breast specimens was computed using a k-space method [Tabei et al., J. Acoust. Soc. Am. 111, 53–63 (2002)] and waveforms were sampled at 1-mm intervals along the propagation direction. Arrival time, amplitude, and coherence fluctuations were correlated with scattering from distinct structures. This observation was most apparent when the first derivatives of those parameters with respect to the propagation direction were compared with the connective tissue architecture in the specimens. The assumption underlying time-shift screen models that aberration arises from smooth fluctuations in the acoustic properties of tissue merits reexamination. [Research supported by an NSERC Discovery Grant.]
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".