Optoacoustic signal amplitude and frequency spectrum analysis laser heated bovine liver ex vivo
Bibliographic record
Abstract
Optoacoustic imaging is being investigated as a potential tool for monitoring the onset and progression of laser thermal therapy. In this study OA images were acquired from ex vivo bovine liver using a reverse-mode OA imaging system consisting of a pulsed laser operating at 775 nm, and an 8 element annular array ultrasound transducer. LTT was performed with an 810 nm laser at 4 W for five minutes. OA signals were acquired for two minutes prior to, five minutes during, and seven minutes post treatment at a rate of 2 Hz. Treatment induced effects were identified based on the OA signal amplitude in combination with spectral analysis of the OA radio frequency (RF) data. The OA signal amplitude was compared with the measured tissue temperatures. Spectrum analysis commonly performed on ultrasound backscatter RF data, which calculate the spectral midband fit, slope, and intercept of the data was used to quantify the changes in the photoacoustic RF signal. The spectral midband fit and intercept increased on average 11 dB and 10 dB respectively. The amplitude of the OA signals increased during treatment on average 350%. However, posttreatment, the response varied. The results of this study support our hypothesis that LTT causes detectable changes in the amplitude and frequency components of OA signals. Both of these parameters may provide independent information about tissue state. These results demonstrate the potential of OA detection for monitoring LTT.
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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 | 0.000 | 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) | 0.001 | 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".