Poster — Thur Eve — 62: Analysis of a Photoacoustic Imaging System by Singular Value Decomposition
Bibliographic record
Abstract
Photoacoustic imaging (PAI) is a hybrid imaging modality capable of producing contrast similar to optical imaging techniques but with increased penetration depth and resolution in turbid media by encoding the optical information as acoustic waves. PAI employs a pulsed laser to diffusely irradiate a volume of interest making the resulting images inherently three‐dimensional. While PAI is a relatively new field, potential applications include the characterization of tumours surrounded by soft tissue, such as breast tissue, as well as imaging vasculature structure. We have developed a PAI system that utilizes a staring, hemispherical array of detectors combined with parallel data acquisition and an iterative image reconstruction algorithm to produce three‐dimensional photoacoustic images using only a single laser pulse. Since our imaging system collects a limited number of data projections and has a shift‐variant response through object space, our objective was to characterize system performance beyond classic metrics such as sensitivity, resolution and contrast by implementing singular value decomposition. Using a robotically placed photoacoustic point source we experimentally captured the imaging operator over a defined object space. Decomposition of the imaging operator was done via singular value decomposition and provided insight into the capability of the PAI system to reconstruct objects and the inherent sensitivity of the PAI system to those objects. Preliminary reconstruction of simple objects is shown utilizing the calibration data in our reconstruction algorithm.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".