Brachytherapy needle deflection evaluation and correction
Bibliographic record
Abstract
In prostate brachytherapy, an 18-gauge needle is used to implant radioactive seeds. This thin needle can be deflected from the preplanned trajectory in the prostate, potentially resulting in a suboptimum dose pattern and at times requiring repeated needle insertion to achieve optimal dosimetry. In this paper, we report on the evaluation of brachytherapy needle deflection and bending in test phantoms and two approaches to overcome the problem. First we tested the relationship between needle deflection and insertion depth as well as whether needle bending occurred. Targeting accuracy was tested by inserting a brachytherapy needle to target 16 points in chicken tissue phantoms. By implanting dummy seeds into chicken tissue phantoms under 3D ultrasound guidance, the overall accuracy of seed implantation was determined. We evaluated methods to overcome brachytherapy needle deflection with three different insertion methods: constant orientation, constant rotation, and orientation reversal at half of the insertion depth. Our results showed that needle deflection is linear with needle insertion depth, and that no noticeable bending occurs with needle insertion into the tissue and agar phantoms. A 3D principal component analysis was performed to obtain the population distribution of needle tip and seed position relative to the target positions. Our results showed that with the constant orientation insertion method, the mean needle targeting error was 2.8 mm and the mean seed implantation error was 2.9 mm. Using the constant rotation and orientation reversal at half insertion depth methods, the deflection error was reduced. The mean needle targeting errors were 0.8 and 1.2 mm for the constant rotation and orientation reversal methods, respectively, and the seed implantation errors were 0.9 and 1.5 mm for constant rotation insertion and orientation reversal methods, respectively.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".