Analysis of patient repositioning accuracy in precision radiation therapy using automated image fusion
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Bibliographic record
Abstract
This work describes a rapid and objective method of determining repositioning error during the course of precision radiation therapy using off-line CT imaging and automated mutual-information image fusion. The technique eliminates the variability associated with manual identification of anatomical landmarks by observers. A phantom study was conducted to quantify the accuracy of the image co-registration-based analysis itself. For CT voxel dimensions of 0.65 x 0.65 x1.0 mm3, the method is shown to detect translations with an accuracy of 0.5 mm in the anterior-posterior and lateral dimensions and 0.8 mm in the superior-inferior dimension. Phantom rotation in the coronal plane was detected to within 0.5 degrees of expected values. The analysis has been applied to eight radiotherapy patients at two independent clinics, each immobilized by the same system for cranial stereotactic radiotherapy and CT-imaged once per week over the five- to six-week course of treatment. Among all patients, the ranges of translation in the anterior-posterior, lateral, and superior-inferior dimensions were -0.91 mm to 0.77 mm, -0.66 mm to 1.02 mm, and -2.24 mm to 3.47 mm, respectively. Considering all patients and CT scans, the standard deviations of translation were 0.42 mm, 0.47 mm, and 1.36 mm in the anterior-posterior, lateral, and superior-inferior dimensions, respectively. The ranges of patient rotation about the superior-inferior, left-right, and anterior-posterior axes were -2.84 to 2.62 degrees, -1.74 degrees to 1.96 degrees, and -1.78 degrees to 1.42 degrees, respectively.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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