Integration of on-line imaging, plan adaptation and radiation delivery: proof of concept using digital tomosynthesis
Bibliographic record
Abstract
The main objective of this manuscript is to propose a new approach to on-line adaptive radiation therapy (ART) in which daily image acquisition, plan adaptation and radiation delivery are integrated together and performed concurrently. A method is described in which on-line ART is performed based on intra-fractional digital tomosynthesis (DTS) images. Intra-fractional DTS images were reconstructed as the gantry rotated between treatment positions. An edge detection algorithm was used to automatically segment the DTS images as the gantry arrived at each treatment position. At each treatment position, radiation was delivered based on the treatment plan re-optimized for the most recent DTS image contours. To investigate the feasibility of this method, a model representing a typical prostate, bladder and rectum was used. To simulate prostate deformations, three clinically relevant, non-rigid deformations (small, medium and large) were modeled by systematically deforming the original anatomy. Using our approach to on-line ART, the original treatment plan was successfully adapted to arrive at a clinically acceptable plan for all three non-rigid deformations. In conclusion, we have proposed a new approach to on-line ART in which plan adaptation is performed based on intra-fractional DTS images. The study findings indicate that this approach can be used to re-optimize the original treatment plan to account for non-rigid anatomical deformations. The advantages of this approach are 1) image acquisition and radiation delivery are integrated in a single gantry rotation around the patient, reducing the treatment time, and 2) intra-fractional DTS images can be used to detect and correct for patient motion prior to the delivery of each beam (intra-fractional patient motion).
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".