SU-FF-T-549: Virtual Simulation of Frame Placement for Gamma Knife® PerfexionTM
Bibliographic record
Abstract
PURPOSE: To optimize the frame placement for patients receiving stereotactic radio surgery (SRS) using GammaKnife® Perfexion™, a simulation program was developed and its accuracy of collision clearance was compared with the planning system, Leksel Gamma Plan 8.0 (LGP). METHODS AND MATERIALS: A simulation program was designed in Matlab to do the following: import patient DICOM images with or without frame, automatically measure skull dimension from the images, simulate the frame, post, and pin placement, create isocenters, and check for collision for each isocenter. The program employs a graphical user interface that can be compiled to run on a personal computer. MR images from ten study patients were imported into the simulation program. Frame placement at the time of treatment was reproduced using the MR fiducial marker of the image in the simulation program. Post and pin length measured at the time of frame setup were used in the simulation. A total of 600 isocenters were selected for the comparison of clearance computed from the simulation program and from LGP. RESULTS: Average clearance at 600 isocenters tested was 6.7mm (standard deviation (SD) of 2.6 mm) from LGP and 6.9 mm (SD of 2.5 mm) from the simulation program. Mean differences between the computed clearance from the simulation program and LGP were −0.6, −0.3, 0.9 and 0.9 mm in anterior left (AL), anterior right (AR), posterior left (PL) and posterior right (PR) respectively. Standard deviations of the differences were 0.9, 1.0, 1.9, and 1.9 mm in the AL, AR, PL and PR directions respectively. CONCLUSION: A simulation program for virtual frame placement was developed to guide an optimal frame setup for patients receiving SRS using gamma knife Perfexion™. It is a reliable tool that can guide optimal frame setup to reduce the chance of collision.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".