Navigated non-contact fluorescence tomography
Bibliographic record
Abstract
Abstract A non-contact approach for diffuse optical tomography (DOT) has been developed for on-demand image updates using surgical navigation technology. A stereoscopic optical tracker provides real-time localization of reflective spheres mounted to a laser diode and near-infrared camera. Standard camera calibration is combined with tracking data to determine the intrinsic camera parameters (focal length, principal point and non-linear lens distortion) and the tracker-to-camera transform. Tracker-to-laser calibration is performed using images of laser beam intersection with a tracked calibration surface. Source and detector positions for a finite-element DOT implementation are projected onto the boundary elements of the tissue mesh by finding ray-triangle intersections. A multi-stage model converts camera counts to surface flux by accounting for lens aperture settings, fluorescence filter transmittance, photodetector quantum efficiency, photon energy, exposure time, readout offset and camera gain. The image-guidance framework was applied to an in-house optical tomography system configured for indocyanine green (ICG) fluorescence. Mean target registration errors for camera and laser calibration were less than 1 mm. Surface flux measurements of total reflectance and fluorescence in Intralipid-based fluorescence phantoms (0–2 μ g ml −1 ) had mean errors of 3.1% and 4.4%, respectively, relative to diffusion theory predictions. Spatially-resolved reflectance measurements in a calibrated optical phantom agreed with theory for radial distances up to 25 mm from the laser source. Inverse fluorescence reconstructions of a sub-surface fluorescence target confirmed the localization accuracy (average target centroid error of 0.44 mm). This translational research system is under investigation for clinical applications in head and neck surgery, including oral cavity tumor resection, lymph node mapping and free-flap perforator assessment.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".