Bibliographic record
Abstract
This work demonstrates the feasibility of Photoacoustic tomography (PAT) and real-time photoacoustic (PA) monitoring using a single transducer prototype system to detect and/or monitor tumour growth using low absorbing targets embedded in turbid phantom and thermal lesions in tissue. A single transducer PA prototype system is build utilizing a laser system producing light in the near infra-red while untrasonic transducers detects the PA pressure waves generated. The ability to image tissue using PAT is initially demonstrated using gelatin phantoms with targets of similar optical properties to native and coagulated prostate tissue. Next, lesions in bovine muscle tissue and bovine liver are also imaged demonstrating the effectiveness of PAT tp detect lesions during thermal therapy (TT). Selective imaging is shown by varying the optical wavelength to preferentially absorb light and target specific structures which in turn produce high contrast after image reconstruction. Finally, the capability of using PA to monitor TT is explored by measuring the changes in the optical and mechanical properties of tissue equivalent albumen phantoms as a function of thermal dose on PA signals, thereby demonstrating the real time capability of this modality to monitor TT.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".