Clinical implementation of a digital tomosynthesis‐based seed reconstruction algorithm for intraoperative postimplant dose evaluation in low dose rate prostate brachytherapy
Bibliographic record
Abstract
PURPOSE: The low dose rate brachytherapy procedure would benefit from an intraoperative postimplant dosimetry verification technique to identify possible suboptimal dose coverage and suggest a potential reimplantation. The main objective of this project is to develop an efficient, operator-free, intraoperative seed detection technique using the imaging modalities available in a low dose rate brachytherapy treatment room. METHODS: This intraoperative detection allows a complete dosimetry calculation that can be performed right after an I-125 prostate seed implantation, while the patient is still under anesthesia. To accomplish this, a digital tomosynthesis-based algorithm was developed. This automatic filtered reconstruction of the 3D volume requires seven projections acquired over a total angle of 60 degrees with an isocentric imaging system. RESULTS: A phantom study was performed to validate the technique that was used in a retrospective clinical study involving 23 patients. In the patient study, the automatic tomosynthesis-based reconstruction yielded seed detection rates of 96.7% and 2.6% false positives. The seed localization error obtained with a phantom study is 0.4 +/- 0.4 mm. The average time needed for reconstruction is below 1 min. The reconstruction algorithm also provides the seed orientation with an uncertainty of 10 degrees +/- 8 degrees. The seed detection algorithm presented here is reliable and was efficiently used in the clinic. CONCLUSIONS: When combined with an appropriate coregistration technique to identify the organs in the seed coordinate system, this algorithm will offer new possibilities for a next generation of clinical brachytherapy systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".