A vision system for patient positioning in radiation therapy
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The paper outlines a new approach for positioning a patient on the treatment table for radiation therapy sessions. The vision approach utilizes lasers and cameras for positioning and has several advantages over the conventional methods. Design/methodology/approach The positioning is accomplished by comparison of a set of computed tomography (CT) contours (acquired from the patient) with a set of corresponding contours acquired by a 3D vision system from the same region of the patient's body. The overall positioning error calculated by the iterative closest point algorithm is used to reorient the treatment table. Various issues related to the acquisition and generation of the 3D spatial data are discussed. Findings Positioning is accurate and can detect small movement in the patient's position. Research limitations/implications Testing was done on a cast of a human torso and additional testing is required on in a hospital environment to fully test the efficiency of the approach. Practical implications The method merges data readily available from standard CT imaging systems and 3D imaging systems. Therefore, the additional hardware requirements are minimal. The system integrates well with existing hardware, software and treatment practices. Originality/value The method introduces a new approach to patient positioning employing a combination of sensor technologies. The approach is accurate, reliable, consumes less time and most importantly prevents the use of X‐rays for patient positioning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it