WE‐G‐BRCD‐08: Virtual Couch Shift (VCS) by Online Plan Re‐Optimization for the MRI Linear Accelerator
Bibliographic record
Abstract
PURPOSE: With the MRI accelerator it will be possible to get continuous patient anatomy updates, ranging from organ deformation to patient translation. To compensate for translations, one can re-optimize the treatment plan based on the online MRI images. Consequently, the IMRT optimization system should be fast and robust enough to generate daily a clinically acceptable plan to perform this 'virtual couch shift (VCS)'. METHODS: The system uses a GPU based Monte-Carlo dose engine (GPUMCD) for online beamlet generation in a 1.5 T magnetic field and a fast inverse dose optimization algorithm (FIDO). For four phantom and two clinical cases (cervix and kidney), we generated clinically acceptable plans. The given plans are regenerated after a series of x, y, and z translations (up to 34 mm) of the patient anatomy, without adapting the optimization constraints as used during the initial optimization. The differences between the original plan and the regenerated plans are evaluated by using the gamma criterion and the relative D99 target coverage. RESULTS: The system accurately reproduced the initial dose distribution after translating the phantom and patient anatomies. The gamma criterion of 2%/2 mm is satisfied for 99.2% all target voxels and for 97.2% for all OAR voxels. The relative D99 differences are almost 0.0 with a small standard deviation. With current hardware, a 7 beam cervix beamlet generation and IMRT optimization takes 141 seconds, the kidney case takes only 14 seconds. CONCLUSIONS: We developed a system which is fast and accurate enough to perform a VCS by online re-optimization for the MRI accelerator. Currently we are adding sequencing to the system. We expect that this method can also be used for compensating patient rotations and tissue deformation and with this go towards realtime adaptive treatment planning and delivery.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".