Intraoperative cone-beam CT spatial priors for diffuse optical fluorescence tomography
Bibliographic record
Abstract
A hybrid system for intraoperative cone-beam CT (CBCT) imaging and continuous-wave fluorescence tomography (FT) has been developed using an image-guidance framework. Intraoperative CBCT images with sub-millimeter spatial resolution are acquired with a flat-panel C-Arm. Tetrahedral meshes are generated from CBCT for finite element method implementation of diffuse optical tomography (NIRFAST). Structural data from CBCT is incorporated directly into the optical reconstruction process using Laplacian-type regularization ('soft spatial priors'). Experiments were performed using an in-house optical system designed for indocyanine green (ICG) fluorescence. A dynamic non-contact geometry was achieved using a stereoscopic optical tracker for real-time localization of a laser diode and CCD camera. Source and detector positions were projected onto the boundary elements of the tissue mesh using algorithms for ray-triangle intersection and camera lens calibration. Simulation studies showed the capabilities of a soft-prior approach, even in the presence of segmentation uncertainties. Experiments with ICG targets embedded in liquid phantoms determined the improvements in the quantification of the fluorophore yield, with errors of 85% and <20% for no priors and spatial priors, respectively. Similar results were observed with the ICG target embedded in ex vivo porcine loin, with errors of 52% and 12%, respectively. A proof-of-principal animal study was performed in a VX2-tumor in vivo rabbit model using liposomal nanoparticles co-encapsulating contrast for CT (iohexol) and fluorescence (ICG) imaging. Fusion of CBCT and FT reconstructions demonstrated concurrent anatomical and functional delineations of contrast enhancement around the periphery of the buccal tumor. These developments motivate future clinical translation of the FT system into an ongoing CBCT-guided head and neck surgery trial.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".