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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".