TU‐C‐220‐04: Photoacoustic Scanning Tomography (PHAST) with Coded Optical Excitation: Theory and Experiment
Bibliographic record
Abstract
Photoacoustic (PA) imaging of biological tissues is emerging as a novel diagnostic modality that relies on noninvasive detection of tissue optical contrast, which may be related to specific diseases. Generation of acoustic waves in response to intensity‐modulated laser irradiation of targeted tissues constitutes the basic principle of photoacoustic imaging. A number of clinically important applications are concerned with the maximum imaging depth and image contrast that can be achieved using the PA method. In the present talk, various modes of laser‐induced acoustic wave generation are reviewed with particular attention given to coded optical excitation and frequency‐domain signal processing methods. Advantages of specific forms of optical modulation with emphasis on the signal‐to‐noise ratio, imaging contrast, and maximum imaging depth will be analyzed theoretically and compared with experimental data. The dual‐mode (time and frequency domain) photoacoustic scanner utilizing a multi‐element transducer array will be presented with results obtained using tissue‐simulating phantoms and an ex‐vivo animal model. Learning objectives: 1. Understand the principles of photoacoustic imaging with coded optical excitation of tissue chromophores. 2. Learn about instrumentation, signal processing and image formation using the PA response to custom optical modulation waveforms. 3. Understand issues related to PA system design and potential clinical applications.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".