Accuracy of Off-Line Bioluminescence Imaging to Localize Targets in Preclinical Radiation Research
Bibliographic record
Abstract
In this study, we investigated the accuracy of using off-line bioluminescence imaging (BLI) and tomography (BLT) to guide irradiation of small soft tissue targets on a small animal radiation research platform (SARRP) with on-board cone beam CT (CBCT) capability. A small glass bulb containing BL cells was implanted as a BL source in the abdomen of 11 mouse carcasses. Bioluminescence imaging and tomography were acquired for each carcass. Six carcasses were setup visually without immobilization and 5 were restrained in position with tape. All carcasses were setup in treatment position on the SARRP where the centroid position of the bulb on CBCT was taken as "truth". In the 2D visual setup, the carcass was setup by aligning the point of brightest luminescence with the vertical beam axis. In the CBCT assisted setup, the pose of the carcass on CBCT was aligned with that on the 2D BL image for setup. For both 2D setup methods, the offset of the bulb centroid on CBCT from the vertical beam axis was measured. In the BLT-CBCT fusion method, the 3D torso on BLT and CBCT was registered and the 3D offset of the respective source centroids was calculated. The setup results were independent of the carcass being immobilized or not due to the onset of rigor mortis. The 2D offset of the perceived BL source position from the CBCT bulb position was 2.3 mm ± 1.3 mm. The 3D offset between BLT and CBCT was 1.5 mm ± 0.9 mm. Given the rigidity of the carcasses, the setup results represent the best that can be achieved with off-line 2D BLI and 3D BLT. The setup uncertainty would require the use of undesirably large margin of 4-5 mm. The results compel the implementation of on-board BLT capability on the SARRP to eliminate setup error and to improve BLT accuracy.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".