Automated intraoperative calibration for prostate cancer brachytherapy
Bibliographic record
Abstract
PURPOSE: Prostate cancer brachytherapy relies on an accurate spatial registration between the implant needles and the TRUS image, called "calibration". The authors propose a new device and a fast, automatic method to calibrate the brachytherapy system in the operating room, with instant error feedback. METHODS: A device was CAD-designed and precision-engineered, which mechanically couples a calibration phantom with an exact replica of the standard brachytherapy template. From real-time TRUS images acquired from the calibration device and processed by the calibration system, the coordinate transformation between the brachytherapy template and the TRUS images was computed automatically. The system instantly generated a report of the target reconstruction accuracy based on the current calibration outcome. RESULTS: Four types of validation tests were conducted. First, 50 independent, real-time calibration trials yielded an average of 0.57 ± 0.13 mm line reconstruction error (LRE) relative to ground truth. Second, the averaged LRE was 0.37 ± 0.25 mm relative to ground truth in tests with six different commercial TRUS scanners operating at similar imaging settings. Furthermore, testing with five different commercial stepper systems yielded an average of 0.29 ± 0.16 mm LRE relative to ground truth. Finally, the system achieved an average of 0.56 ± 0.27 mm target registration error (TRE) relative to ground truth in needle insertion tests through the template in a water tank. CONCLUSIONS: The proposed automatic, intraoperative calibration system for prostate cancer brachytherapy has achieved high accuracy, precision, and robustness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".