The Use of Cone Beam Computed Tomography for Image Guided Gamma Knife Stereotactic Radiosurgery: Initial Clinical Evaluation
Bibliographic record
Abstract
PURPOSE: The present study used cone beam computed tomography (CBCT) to measure the inter- and intrafraction uncertainties for intracranial stereotactic radiosurgery (SRS) using the Leksell Gamma Knife (GK). METHODS AND MATERIALS: Using a novel CBCT system adapted to the GK radiosurgery treatment unit, CBCT images were acquired immediately before and after treatment for each treatment session within the context of a research ethics board-approved prospective clinical trial. Patients were immobilized in the Leksell coordinate frame (LCF) for both volumetric CBCT imaging and GK-SRS delivery. The relative displacement of the patient's skull to the stereotactic reference (interfraction motion) was measured for each CBCT scan. Differences between the pre- and post-treatment CBCT scans were used to determine the intrafraction motion. RESULTS: We analyzed 20 pre- and 17 post-treatment CBCT scans in 20 LCF patients treated with SRS. The mean translational pretreatment setup error ± standard deviation in the left-right, anteroposterior, and craniocaudal directions was -0.19 ± 0.32, 0.06 ± 0.27, and -0.23 ± 0.2 mm, with a maximum of -0.74, -0.53, and -0.68 mm, respectively. After an average time between the pre- and post-treatment CBCT scans of 82 minutes (range 27-170), the mean intrafraction error ± standard deviation for the LCF was -0.03 ± 0.05, -0.03 ± 0.18, and -0.03 ± 0.12 mm in the left-right, anteroposterior, and craniocaudual direction, respectively. CONCLUSIONS: Using CBCT on a prototype image guided GK Perfexion unit, we were able to measure the inter- and intrafraction positional changes for GK-SRS using the invasive frame. In the era of image guided radiation therapy, the use of CBCT image guidance for both frame- and non-frame-based immobilization systems could serve as a useful quality assurance tool. Our preliminary measurements can guide the application of achievable thresholds for inter- and intrafraction discrepancy when moving to a frameless approach.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".