Evaluating a potential technique with local optical flow vectors for automatic organ-at-risk (OAR) intrusion detection and avoidance during radiotherapy
Bibliographic record
Abstract
Various techniques of deep inspiration breath hold (DIBH) have been used to mitigate the likelihood and risk of exposing the heart, an organ-at-risk (OAR) for unintended radiation during left breast radiotherapy. However, issues of reproducibility of these techniques warrant further investigation into the feasibility of detecting the intrusion of an OAR into the treatment field during intra-fractional treatment delivery. The increase of high-dose, low-fraction radiotherapy treatments makes it important to immediately adapt treatment once an OAR is detected in the treatment field. This proof-of-concept implementation includes an algorithm that detects and tracks the motion at the edges of a treatment field and a control algorithm that adapts the treatment aperture according to the motion detected. In accordance to the AAPM Task-Group (TG-132) report, image registration techniques should be verified with virtual and physical phantoms prior to clinical application. Since most OARs move as a result of respiration-induced motion, we have used a lung phantom to generate images of a generic OAR intruding into a treatment field with known velocity. The phantom was programmed to move with sinusoidal and lung patient tumor motion patterns and the accuracy of intrusion tracking and MLC adaptation were benchmarked with the ground truth-programmed motion of the OAR. The motions were recorded with an electronic portal imaging device (EPID). An optimal cluster size of 9 × 9 motion vectors was found to provide the smallest average absolute position error of 0.3 mm. A strong linear correlation between the adapted MLC leaves and the actual OAR position was observed. The algorithm had a mean position tracking error of -0.4 ± 0.3 mm and a precision of 1.1 mm. It is possible to adapt MLC leaves based on the motion detected at the edges of the irradiated field, and it would be feasible to shield an unplanned intrusion of an OAR into the treatment field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".