A phantom study to assess accuracy of needle identification in real-time planning of ultrasound-guided high-dose-rate prostate implants
Bibliographic record
Abstract
PURPOSE: High-dose-rate brachytherapy of the prostate is commonly performed using transrectal ultrasound (US) guidance, with CT imaging used for needle reconstruction and treatment planning. Transrectal ultrasound images can, however, be used for the entire process, allowing treatment without changes in the patient position. This study assesses needle reconstruction accuracy using US images. METHODS AND MATERIALS: Prostate phantoms were implanted with 10-18 needles. Three-dimensional US images were acquired, and needles were reconstructed using specialized software. A CT scan was also obtained. The image sets were registered and needle reconstruction errors were assessed. A dose plan was obtained using the US images and the dwell times were transferred to the CT reconstruction to obtain the true "delivered dose," which was evaluated using standard dosimetric parameters. RESULTS: Two sources of error were identified. First, reconstruction based on the bright echoes in the US images introduces a systematic error because these echoes correspond to the proximal wall of the needle, and not the center of the needle channel. If left uncorrected, this shift can lead to an underestimate of urethral doses. Second, incorrect needle tip identification can occur in the cranial-caudal direction. Errors up to 5.8mm were observed. A measurement of needle lengths protruding beyond the template can be used to compensate for this. CONCLUSIONS: Factors limiting the accuracy of US-based needle reconstruction have been identified. Once recognized, these errors can be corrected for, resulting in accurate implant geometry. This facilitates a treatment technique combining excellent anatomic definition, minimal prostate motion, and accurate dose planning and delivery.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".