Defining a magnetic resonance scan sequence for permanent seed prostate brachytherapy postimplant assessment
Bibliographic record
Abstract
PURPOSE: We describe a magnetic resonance (MR) scan sequence for prostate brachytherapy postimplant assessment. METHODS AND MATERIALS: One brachytherapy team at the British Columbia Cancer Agency has incorporated MR-CT fusion into their permanent seed prostate brachytherapy quality assurance procedure. Several attempts were required to ensure that the diagnostic MR scanner at the adjoining general hospital performed the desired sequence, providing many examples of suboptimal scans and underlining the pitfalls for a center trying to incorporate the use of MR scanning into their brachytherapy program. RESULTS: The recommended sequence (Fast Spin Echo T2-weighted, repetition time [TR]/echo time [TE] 4500/90, echo train length [ETL] 10, 20×20 field of view [FOV], 80 bandwidth [BW]) is associated with superior edge detection when compared with those images in which a typical diagnostic sequence was used. The use of a low bandwidth sequence does not compromise edge detection or seed identification when compared with a higher bandwidth. CONCLUSIONS: We have defined a magnetic resonance imaging sequence, which appears to optimize both prostate delineation and identification of seeds, lending itself to straightforward fusion with CT images and allowing for less uncertainty in permanent seed prostate brachytherapy quality assurance.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".