Dynamic range enhancement for diffuse optical spectroscopy in breast scanning applications
Bibliographic record
Abstract
Diffuse Optical Breast Scanner (DOB-Scan) probe utilizes a linear charge-coupled device (CCD) array as the detector, measuring back-scattered light intensity above the breast tissue to create cross-sectional concentration images for different constituents. The response received at each pixel of the sensor is determined by optical properties of the scattering medium, source-detector distance, integration time of CCD array, and intensity of the light source. However, the performance of the probe is limited by the inherent electronic properties of CCD array, which cause saturation at high response region and high noise level at low response region. In this paper, an algorithm to enhance the dynamic range of the CCD array is presented. The objective is to maximize the dynamic range of the CCD array without any data loss while minimizing the noise-to-signal ratio where the response of the CCD array is relatively low. The desired output can be achieved by capturing multiple sets of data with different integration time and light intensity settings, ensuring the best CCD performance in each pixel range of interest. The profile of CCD array’s linearity and optical power measurement of the light source allow different sets of data to be translated into the same scale and joined accordingly into one. A series of phantom studies are conducted and confirm the feasibility of the probe’s dynamic range intensification.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".