Acoustic imaging of thick biological tissue
Bibliographic record
Abstract
Up to now, biomedical imaging with ultrasound for observing a cellular tissue structure has been limited to very thinly sliced tissue at very high ultrasonic frequencies, i.e., 1 GHz. In this paper, we present the results of a systematic study to use a 150 to 200 MHz frequency range for thickly sliced biological tissue. A mechanical scanning reflection acoustic microscope (SAM) was used for obtaining horizontal cross-sectional images (C-scans) showing cellular structures. In the study, sectioned specimens of human breast cancer and tissues from the small intestine were prepared and examined. Some accessories for biomedical application were integrated into our SAM (Sonix HS-1000 and Olympus UH-3), which operated in pulse-wave and tone-burst wave modes, respectively. We found that the frequency 100 to 200 MHz provides optimal balance between resolution and penetration depth for examining the thickly sliced specimens. The images obtained with the lens focused at different depths revealed cellular structures whose morphology was very similar to that seen in the thinly sectioned specimens with optical and scanning acoustic microscopy. The SAM operation in the pulse-echo mode permits the imaging of tissue structure at the surface, and it also opens up the potential for attenuation imaging representing reflection from the substrate behind the thick specimen. We present such images of breast cancer proving the method's applicability to overall tumor detection. SAM with a high-frequency tone-burst ultrasonic wave reveals details of tissue structure, and both methods may serve as additional diagnostic tools in a hospital environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".