Cone Beam CT (CBCT) Evaluation of Inter- and Intra-Fraction Motion for Patients Undergoing Brain Radiotherapy Immobilized using a Commercial Thermoplastic Mask on a Robotic Couch
Bibliographic record
Abstract
Patients receiving fractionated intensity-modulated radiation therapy (IMRT) for brain tumors are often immobilized with a thermoplastic mask; however, masks do not perfectly re-orient the patient due to factors including the maximum pressure which can be applied to the face, deformations of the mask assembly, patient compliance, etc. Consequently, ~3-5mm PTV margins (beyond the CTV) are often recommended. We aimed to determine if smaller PTV margins are feasible using mask immobilization coupled with 1) a gantry mounted CBCT image guidance system and 2) position corrections provided by a full six-degree of freedom (6-DOF) robotic couch. A cohort of 34 brain tumor patients was treated with fractionated IMRT. After the mask set-up, an initial CBCT was obtained and registered to the planning CT. The robotic couch corrected the misalignments in all 6-DOF and a pre-treatment verification CBCT was then obtained. The results indicated a repositioning alignment within our threshold of 1.5 mm (3D). Treatment was subsequently delivered. A post-treatment CBCT was obtained to quantify intra-fraction motion. Initial, pre-treatment and post-treatment CBCT image data was analyzed. A total of 505 radiation fractions were delivered to the 34 patients resulting in ~1800 CBCT scans. The initial median 3D (magnitude) set-up positioning error was 2.60 mm. Robotic couch corrections reduced the 3D median error to 0.53 mm prior to treatment. Intra-fraction movement was responsible for increasing the median 3D positioning error to 0.86 mm, with 8% of fractions having a 3D positioning error greater than 2 mm. Clearly CBCT image guidance coupled with a robotic 6-DOF couch dramatically improved the positioning accuracy for patients immobilized in a thermoplastic mask system; however, such intra-fraction motion would be too large for single fraction radiosurgery.
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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 | 0.001 | 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) | 0.002 | 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".