Poster - Thurs Eve-31: Clinical implementation and experience with EPID-based precision isocentre localization
Bibliographic record
Abstract
Modern linear accelerators contain multiple isocentres, defined by the mechanical motions of gantry, collimator and table. Isocentre localization for these motions has been performed using film and manual evaluations which have difficulty in relating the individual motions. To address these limitations, we have developed an EPID based technique to measure the isocentre position for each of the treatment unit motions. This technique uses the projected position of a radio-opaque marker at the isocentre in a series of MV images to determine the motion of the isocentre. This analytical procedure has been implemented in the clinic using a MatLab code to automatically analyze images and determine both the isocentre position and motion about the mean for each of gantry, collimator and table. Results of isocentre measurements for 18 machines from 2 different vendors at 2 separate clinics are reported. These measurements show that while the position of the mean isocentres are contained within a 2mm sphere, combinations of gantry, table and collimator rotations can be found that result in treatment isocentres more than 2mm apart. Results for a treatment unit, which underwent a recent equipment upgrade, are also presented that show a small change in the location of the gantry relative to the table isocentre. The implementation of this of isocentre localization technique has provided important clinical information which can be efficiently completed in less than an hour. This information is an important consideration in monitoring the changes and in assessing the treatment precision that can be obtained.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".