Effect of tissue optics on wavelength optimization for quantum dot-based surface and subsurface fluorescence imaging
Bibliographic record
Abstract
Optimization is an important but relatively unexplored aspect of contrast-enhanced fluorescence imaging, since minimizing contrast agent usage reduces the associated cost and potential toxicity. In a previous study, the authors developed a quantitative experimental approach to optimize quantum dot (QD)-based imaging using homogenized liver as a model tissue. In this follow-up study, the authors further extend and validate the approach using eight different tissues and five QDs emission wavelengths, and introduce quantitative imaging performance metrics, namely the threshold QD concentration and wavelength optimization gain. These metrics allow quantification of the improvements through spectral optimization in terms of reduced QD dose and identify the conditions that make the optimization process worthwhile. The authors show that, for most tissues, the most important parameter to optimize is the emission wavelength, yielding improvements of up to four orders of magnitude, followed by the excitation wavelength (up to 20-fold improvement) and the excitation filter bandwidth (up to 50% improvement). The authors also observe, by means of the optimization gain metric, that tissues exhibiting both high autofluorescence and strong pigmentation are generally better candidates for excitation wavelength optimization. This work contributes to the development of robust and quantitative dosimetry for QD-based fluorescence imaging near to the tissue surface.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".