Intraoperative 3D ultrasound guidance system for permanent breast seed implantation
Bibliographic record
Abstract
Permanent breast seed implantation (PBSI) is a single-visit technique for accelerated partial breast irradiation that uses a template and needles to implant seeds of Pd-103 under 2D ultrasound (US) guidance. The short treatment time is advantageous given the widely hypothesized link between treatment burden and mastectomy use. However, limitations of 2D US contribute to high operator dependence and seed placement error that we aim to address by developing a 3D US guidance system. A 3D US scanner for PBSI and a mechanism for template localization have been developed and validated. The 3D US system mechatronically moves and tracks a 2D US transducer over a 5 cm translation and 60° tilt, reconstructing the 2D images into a 3D volume as they are acquired. Additionally, a localizing arm, tracked via encoded joints and mounted to the scanner, determines template position by localizing divots on a modified needle template. Volume reconstruction was validated using linear measurements of a grid phantom and volumetric measurements of two surgical cavity phantoms. Localizing arm measurement accuracy was established using a testing jig with divots at known positions. Imaging volume was rigidly registered to scanner geometry using a string phantom mounted to a test jig. Lastly, volunteer scans were conducted to demonstrate clinical applicability. Median linear and average volumetric measurements were within ±1.4% of nominal and ±4.1% of water displacement measurements, respectively. Median measurement accuracy of the localizing arm was 0.475 mm. Imaging volume target registration error was 0.458 mm. Volunteer scans produced clinical quality images.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".