Bibliographic record
Abstract
Purpose: To design a novel phantom compatible with multiple imaging modalities (kV, MV and MRI) for the daily quality assurance (QA) of an MR‐guided linac. Methods: At our institution, we are in the process of installing a decoupled MR‐linac system, consisting of a 1.5T Siemens Espree MR scanner on rails that can be moved and image in the immediate proximity of a Varian TrueBeam linac. The two components are connected via a robotic patient couch. For this configuration, both MR and x‐ray based (kV, MV) imaging modalities will be available and will have to be mutually correlated to confirm the robustness of the RT ensemble. The authors modified the design of on an existing daily QA phantom that is compatible with kV radiographs, MV EPIDs and kV CBCT. New features were added to make the phantom also visible on MRI. The overall design was optimized by means of numerical simulations to remove any significant MR image‐ related artifacts due to magnetic susceptibility‐induced effects. An image software analysis tool was also developed to process the data and capture all kV/MV/MRI unique characteristics. In addition, the software provides the capability of record and track over time the performance of the tests via control charts. Results: The phantom was developed to test kV/MV/MR system coincidence, 3D CBCT and 3D MR independent and cross‐modality mutual registration, kV/MV and MR projection images, kV/MV coincidence, MR/linac laser and light field coincidence, remote table adjustments. Conclusions: A novel phantom along with an image processing software package was developed for the routine daily QA of a linac‐MR on rails radiotherapy system. D. Moseley has a license agreement with Modus Medical, Inc.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".