Performance of a Novel Frameless and Maskless Robotic Head Motion Compensation System for Stereotactic Radiosurgery in a Realistic Clinical Environment with Healthy Volunteers
Bibliographic record
Abstract
PURPOSE: Stereotactic radiosurgery (SRS) is a nonsurgical method for treating brain abnormalities and small tumors. Traditional high-accuracy SRS requires a rigid metal head frame screwed into the skull, which causes discomfort and reduces patient compliance. Thermoplastic masks offer a less invasive alternative but compromise accuracy because of flexing and are often still uncomfortable. To address these issues, we developed a novel robotic head motion compensation (RHMC) device that enables frameless and maskless SRS. METHODS AND MATERIALS: A compact, portable RHMC device was developed that can be quickly attached to or detached from the end of a linear accelerator treatment table. Real-time 6° head position tracking was performed using 3-dimensional surface-guided radiation therapy imaging, which was fed into the robot control computer. Device performance was evaluated by administering virtual SRS treatments to a phantom and 20 healthy volunteers, simulating a clinical environment but without delivering radiation. The primary success metric was defined as maintaining the 6D target position under a 1.0 mm and 1.0° threshold for more than 95% of beam-on time (denoted as DC95%_1.0 mm and 1.0°). RESULTS: Two of the 20 volunteers were excluded because of incompatibility with the RHMC device. Among the remaining 18 volunteers, the DC95%_1.0 mm and 1.0° success metric was achieved in all cases. Without the RHMC device, 9 of the 18 volunteers were able to meet this metric. For a tighter tolerance of DC95%_1.0 mm and 0.5°, 17 volunteers achieved the metric with the RHMC device, compared with 4 without. For a tolerance of 1.0 mm and 1.0°, across all 18 volunteers, the mean and range were 99% and 96% to 100% using the RHMC device, respectively, compared with 73% and 9% to 100% without the RHMC device. CONCLUSIONS: The RHMC device effectively maintained accurate head motion control under simulated clinical conditions, achieving the DC95%_1.0 mm and 1.0° success metric for all suitable candidates. This technology has the potential to enable frameless and maskless SRS delivery within or better than current standard-of-care tolerance guidelines.
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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 | 0.000 | 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".