Development of a geometrically accurate imaging protocol at 3 Tesla MRI for stereotactic radiosurgery treatment planning
Bibliographic record
Abstract
The purpose of this study is to develop a geometrically accurate imaging protocol at 3 T magnetic resonance imaging (MRI) for stereotactic radiosurgery (SRS) treatment planning. In order to achieve this purpose, a methodology is developed to investigate the geometric accuracy and stability of 3 T MRI for SRS in phantom and patient evaluations. Forty patients were enrolled on a prospective clinical trial. After frame placement prior to SRS, each patient underwent 3 T MRI after 1.5 T MRI and CT. MR imaging protocols included a T1-weighted gradient echo sequence and a T2-weighted spin echo sequence. Phantom imaging was performed on 3 T prior to patient imaging using the same set-up and imaging protocols. Geometric accuracy in patients and phantoms yielded comparable results for external fiducial reference deviations and internal landmarks between 3 T and 1.5 T MRI (mean ≤ 0.6 mm; standard deviation ≤ 0.3 mm). Mean stereotactic reference deviations between phantoms and patients correlated well (T1: R = 0.79; T2: R = 0.84). Statistical process control analysis on phantom QA data demonstrated the stability of our SRS imaging protocols, where the geometric accuracy of the 3 T SRS imaging protocol is operating within the appropriate tolerance. Our data provide evidence supporting the spatial validity of 3 T MRI for targeting SRS under imaging conditions investigated. We have developed a systematic approach to achieve confidence on the geometric integrity of a given imaging system/technique for clinical integration in SRS application.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".