Characterization of a Time-Resolved Diffuse Optical Spectroscopy Prototype Using Low-Cost, Compact Single Photon Avalanche Detectors for Tissue Optics Applications
Bibliographic record
Abstract
Time-resolved diffuse optical spectroscopy (TR-DOS) is an increasingly used method to determine the optical properties of diffusive media, particularly for medical applications including functional brain, breast and muscle measurements. For medical imaging applications, important features of new generation TR-DOS systems are low-cost, small size and efficient inverse modeling. To address the issues of low-cost, compact size and high integration capabilities, we have developed free-running (FR) single-photon avalanche diodes (SPADs) using 130 nm silicon complementary metal-oxide-semiconductor (CMOS) technology and used it in a TR-DOS prototype. This prototype was validated using assessments from two known protocols for evaluating TR-DOS systems for tissue optics applications. Following the basic instrumental performance protocol, our prototype had sub-nanosecond total instrument response function and low differential non-linearity of a few percent. Also, using light with optical power lower than the maximum permissible exposure for human skin, this prototype can acquire raw data in reflectance geometry for phantoms with optical properties similar to human tissues. Following the MEDPHOT protocol, the absolute values of the optical properties for several homogeneous phantoms were retrieved with good accuracy and linearity using a best-fitting model based on the Levenberg-Marquardt method. Overall, the results of this study show that our silicon CMOS-based SPAD detectors can be used to build a multichannel TR-DOS prototype. Also, real-time functional monitoring of human tissue such as muscles, breasts and newborn heads will be possible by integrating this detector with a time-to-digital converter (TDC).
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".