Wavelength-Modulated Differential Photoacoustic (WM-DPA) imaging: a high dynamic range modality towards noninvasive diagnosis of cancer
Bibliographic record
Abstract
This study explores wavelength-modulated differential photo-acoustic (WM-DPA) imaging for non-invasive early cancer detection via sensitive characterization of functional information such as hemoglobin oxygenation (sO2) levels. Well-known benchmarks of tumor formation such as angiogenesis and hypoxia can be addressed this way. While most conventional photo-acoustic imaging has almost entirely employed high-power pulsed lasers, frequency-domain photo-acoustic radar (FD-PAR) has seen significant development as an alternative technique. It employs a continuous wave laser source intensity-modulated and driven by frequency-swept waveforms. WM-DPA imaging utilizes chirp modulated laser beams at two distinct wavelengths for which absorption differences between oxy- and deoxygenated hemoglobin are minimum (isosbestic point, 805 nm) and maximum (680 nm) to simultaneously generate two signals detected using a standard commercial array transducer as well as a single-element transducer that scans the sample. Signal processing is performed using Lab View and Matlab software developed in-house. Minute changes in total hemoglobin concentration (tHb) and oxygenation levels are detectable using this method since background absorption is suppressed due to the out-of-phase modulation of the laser sources while the difference between the two signals is amplified, thus allowing pre-malignant tumors to become identifiable. By regulating the signal amplitude ratio and phase shift the system can be tuned to applications like cancer screening, sO2 quantification and hypoxia monitoring in stroke patients. Experimental results presented demonstrate WM-DPA imaging of sheep blood phantoms in comparison to single-wavelength FD-PAR imaging. Future work includes the functional PA imaging of small animals in vivo.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".