Quantitative phase‐filtered wavelength‐modulated differential photoacoustic radar tumor hypoxia imaging toward early cancer detection
Bibliographic record
Abstract
Overcoming the limitations of conventional linear spectroscopy used in multispectral photoacoustic imaging, wherein a linear relationship is assumed between the absorbed optical energy and the absorption spectra of the chromophore at a specific location, is crucial for obtaining accurate spatially‐resolved quantitative functional information by exploiting known chromophore‐specific spectral characteristics. This study introduces a non‐invasive phase‐filtered differential photoacoustic technique, wavelength‐modulated differential photoacoustic radar (WM‐DPAR) imaging that addresses this issue by eliminating the effect of the unknown wavelength‐dependent fluence. It employs two laser wavelengths modulated out‐of‐phase to significantly suppress background absorption while amplifying the difference between the two photoacoustic signals. This facilitates pre‐malignant tumor identification and hypoxia monitoring, as minute changes in total hemoglobin concentration and hemoglobin oxygenation are detectable. The system can be tuned for specific applications such as cancer screening and SO2 quantification by regulating the amplitude ratio and phase shift of the signal. The WM‐DPAR imaging of a head and neck carcinoma tumor grown in the thigh of a nude rat demonstrates the functional PA imaging of small animals in vivo. The PA appearance of the tumor in relation to tumor vascularity is investigated by immunohistochemistry. Phase‐filtered WM‐DPAR imaging is also illustrated, maximizing quantitative SO2 imaging fidelity of tissues. Oxygenation levels within a tumor grown in the thigh of a nude rat using the two‐wavelength phase‐filtered differential PAR method. magnified image Oxygenation levels within a tumor grown in the thigh of a nude rat using the two‐wavelength phase‐filtered differential PAR method.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".