Differentiating fatty and non-fatty tissue using photoacoustic imaging
Bibliographic record
Abstract
In this paper, we demonstrate a temporal-domain intensity-based photoacoustic imaging method that can differentiate between fatty and non-fatty tissues. PA pressure intensity is partly dependent on the tissue’s speed of sound, which increases as temperature increases in non-fatty tissue and decreases in fatty tissue. Therefore, by introducing a temperature change in the tissue and subsequently monitoring the change of the PA intensity, it is possible to distinguish between the two types of tissue. A commercial ultrasound system with a 128-element 5-14 MHz linear array transducer and a tunable ND:YAG laser were used to produce PA images. Ex-vivo bovine fat and porcine liver tissues were precooled to below 10°C and then warmed to room-temperature over ~1 hour period. A thermocouple monitored the temperature rise while PA images were acquired at 0.5°C intervals. The averaged intensity of the illuminated tissue region at each temperature interval was plotted and linearly fitted. Liver samples showed a mean increase of 2.82 %/°C, whereas bovine fat had a mean decrease of 6.24 %/°C. These results demonstrate that this method has the potential to perform tissue differentiation in the temporal-domain.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".