Poster - Thurs Eve-35: Error reduction in variable angle implant reconstruction by optimization of imaging geometry
Bibliographic record
Abstract
Movement towards intraoperative dosimetric analysis of permanent seed brachytherapy implants has driven seed localization techniques that may be applied in an operating suite. Variable angle reconstructions with a C-arm are a widely available, cost effective and easily integrated solution but are subject to mechanical limitations on imaging geometry accuracy. Phantom reconstructions of known seed locations were used to show optimization of the imaging geometry for consistency with the observed datasets in multiple variable angle images can be used to obtain more accurate source and detector positions and reduce uncertainty in reconstructed seed positions. Furthermore, the most difficult part of backprojection methods is the matching of corresponding seeds between the variable angle images. With improved accuracy in imaging geometry, tighter constraints on possible seed matches can be used. This reduces the number of potential matches between images. Application to typical clinical data has shown a reduction in the number of possible matches. The benefits are observed when optimizations are performed on very small subsets of matched seeds, allowing a small number of obvious matches due to orientation or peripheral positioning to greatly simplify matching over the remainder of the implant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".