A multi-view time-domain non-contact diffuse optical tomography scanner with dual wavelength detection for intrinsic and fluorescence small animal imaging
Bibliographic record
Abstract
We present a non-contact diffuse optical tomography (DOT) scanner with multi-view detection (over 360°) for localizing fluorescent markers in scattering and absorbing media, in particular small animals. It relies on time-domain detection after short pulse laser excitation. Ultrafast time-correlated single photon counting and photomultiplier tubes are used for time-domain measurements. For light collection, seven free-space optics non-contact dual wavelength detection channels comprising 14 detectors overall are placed around the subject, allowing the measurement of time point-spread functions at both excitation and fluorescence wavelengths. The scanner is endowed with a stereo camera pair for measuring the outer shape of the subject in 3D. Surface and DOT measurements are acquired simultaneously with the same laser beam. The hardware and software architecture of the scanner are discussed. Phantoms are used to validate the instrument. Results on the localization of fluorescent point-like inclusions immersed in a scattering and absorbing object are presented. The localization algorithm relies on distance ranging based on the measurement of early photons arrival times at different positions around the subject. This requires exquisite timing accuracy from the scanner. Further exploiting this capability, we show results on the effect of a scattering hetereogenity on the arrival time of early photons. These results demonstrate that our scanner provides all that is necessary for reconstructing images of small animals using full tomographic reconstruction algorithms, which will be the next step. Through its free-space optics design and the short pulse laser used, our scanner shows unprecedented timing resolution compared to other multi-view time-domain scanners.
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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 | 0.001 |
| 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 | 0.001 |
| 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".