SU‐E‐J‐149: Validation of the Spatial Accuracy of the ExacTrac Adaptive Gating System
Bibliographic record
Abstract
Purpose: To evaluate the positioning accuracy of the BrainLAB ExacTrac Image Guidance System under gating conditions. Methods: Two types of phantoms were used in measurements: an anthropomorphic RANDO head phantom and a BrainLAB ExacTrac Gating Phantom. Our setup included a Varian Novalis Tx radiosurgery system equipped with the ExacTrac 6D IGRT. This system consists of an infrared positioning system for the initial patient positioning and patient tracking, and a stereoscopic kV X‐ray imaging system for final localization using internal markers or anatomy. Uncertainties were broken down into individual components, and the different BrainLAB fusion modalities (internal markers and bony fusion) were used to compare the effect of slice thickness on positional accuracy. Gating uncertainties were deduced with varying tumor motion amplitudes and window sizes in conjunction with a hidden target test. Results: Our results of CT slice thickness dependence for both fusion algorithms with the hidden target test gave similar deviation (<0.7mm), and were reasonably consistent up to a 5 mm slice width. Tumor motion and gating window size yielded an uncertainty of up to 1 mm for the parameters tested. Combining a non‐gating uncertainty of 0.9 mm with the gating uncertainty resulted in a geometrical accuracy of 1.6 ± 0.7 mm for 2.25 cm tumor amplitude and a 30% window size. For tumor motions up to 3 cm and gating window sizes up to 30%, the localization accuracy remained within 2 mm. Conclusions: We have tested the gating window and tumor amplitude effects on the spatial accuracy of the ExacTrac System equipped Novalis Tx linac for stereotactic body radiation therapy. While the CT slice thickness, mechanical deviation of the linac and gating window size contribute to uncertainty, the system provides an external modality that allows for localization accuracy of less than 2 mm for gated delivery.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".