Prostate brachytherapy seed localization using a mobile C-arm without tracking
Bibliographic record
Abstract
The success of prostate brachytherapy depends on the faithful delivery of a dose plan. In turn, intraoperative localization and visualization of the implanted radioactive brachytherapy seeds enables more proficient and informed adjustments to the executed plan during therapy. Prior work has demonstrated adequate seed reconstructions from uncalibrated mobile c-arms using either external tracking devices or image-based fiducials for c-arm pose determination. These alternatives are either time-consuming or interfere with the clinical flow of the surgery, or both. This paper describes a seed reconstruction approach that avoids both tracking devices and fiducials. Instead, it uses the preoperative dose plan in conjunction with a set of captured images to get initial estimates of the c-arm poses followed by an auto-focus technique using the seeds themselves as fiducials to refine the pose estimates. Intraoperative seed localization is achieved through iteratively solving for poses and seed correspondences across images and reconstructing the 3D implanted seeds. The feasibility of this approach was demonstrated through a series of simulations involving variable noise levels, seed densities, image separability and number of images. Preliminary results indicate mean reconstruction errors within 1.2 mm for noisy plans of 84 seeds or fewer. These are attained for additive noise whose standard deviation of the 3D mean error introduced to the plan to simulate the implant is within 3.2 mm.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".