A novel method for automatic detection of patient out‐of‐plane rotation by comparing a single portal image to a reference image
Bibliographic record
Abstract
A novel method for detecting out-of-plane patient rotation by comparing a single portal image to its reference image is presented. Out-of-plane rotation results in an apparent distortion of the anatomy in a portal image. This distortion can be mathematically predicted with the magnification varying at each point in the image. While scaling of points at equal depth is invariant under in-plane rotation or translation, and changes equally in both dimensions for an axial shift of the patient, a change of scaling in only one dimension can be ascribed to an out-of-plane rotation. For the two conditions that are used in this study, it is shown that out-of-plane rotation yields a different scaling of the image in two perpendicular directions and therefore it is feasible to calculate the scale factors as a function of out-of-plane rotation. Conversely the recovery of scale factors in two different directions at the same time would enable the magnitude of the out-of-plane rotation to be recovered. The properties of the Fourier transform of the image are used to align the portal image with the reference image (a simulator image or first approved portal image) prior to the recovery of the scale factors. Correlating the Fourier transform of the portal image on a log-scale with that of the reference image enables the scale factors to be automatically extracted from a single portal image. In the two approaches investigated, out-of-plane rotations of up to 41 degrees and 20 degrees (respectively) have been recovered with a maximum error of 2.4 degrees. This technique could be used to automatically detect patient roll or tilt prior to or during a treatment session.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".