Scattering of high-frequency ultrasound by cells and cell ensembles: In search of the dominant scattering source
Bibliographic record
Abstract
High-frequency ultrasound (HFU) imaging (in the range of 20–60 MHz) has been recently enabled by advances in transducer technology and electronics. These high frequencies afford greater image resolution, on the order of 40 microns, at the expense of penetration depth, limited to a few centimeters. The associated ultrasound wavelengths of HFU are 25–75 microns, on the order of the size of cells. Therefore, it is hypothesized that at these frequencies ultrasound tissue characterization will provide more meaningful information about the physical characteristics of cells and any changes that occur in their structure during cancer treatment. The late Dr. Frederic Lizzi pioneered ultrasound tissue characterization and the seminal papers he published provided the impetus for this work. Here, backscatter from individual cells and tightly packed cell aggregates of the same cells (emulating tumors) are examined and compared to theoretical models of ultrasound backscatter. Recent work with a series of cells lines with different sizes and physical characteristics will be presented. It will be shown that for in vitro and in vivo experiments, the backscatter signal characteristics best correlate with nuclear size.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".