TU‐E‐ValB‐07: A Segmentation and Leaf Sequencing Algorithm for IMAT
Bibliographic record
Abstract
Purpose: To develop an intensity segmentation and leaf sequencing algorithm specifically for intensity‐modulated arc therapy (IMAT), which can be applied to optimized intensity patterns derived from existing commercial IMRT inverse planning software. Methods and Materials: Three phantom cases, as well as a clinical case were planned using a Hi‐Art II (Tomotherapy Inc, WI.) planning station. The end of planning sinograms were then extracted and inputted into our IMAT conversion algorithm. The number of required arcs, deliverable MLC segments for each arc and the relative intensity weighting of each arc were outputted. The number of arcs (modulation) could be controlled by a user parameter, α. The resulting MLC segments were then fed into a fast monte‐carlo dose calculation algorithm, NXEGS (NumeriX, LLC) to obtain 3D dose distributions. Dose statistics (max, min, mean) and dose volume histograms of relevant structures were calculated and compared against the results generated by the Hi‐Art II system. Results: Each plan was converted in under three minutes on a typical desktop PC, with the arc numbers varying between 4 and 15 360° arcs. Qualitatively, the dose distributions obtained from the IMAT plans were similar to the tomotherapy results, as well as planned doses. Quantitatively, the IMAT plans were slightly degraded, with the average dose to normal structures being 7.5% higher for IMAT vs. tomotherapy. However, the IMAT plans generally met planned values, being 9.1% below for maximum doses to normal structures. The number of arcs and therefore the resulting dose distribution could be varied according to α. Conclusions: IMAT segmentation and leaf sequencing produced deliverable IMAT MLC segments and relative arc weights directly from Hi‐Art II optimized plans. The algorithm was computationally efficient, and produced similar dose distributions. Additional optimization could improve resulting dose distributions further. IMAT back‐up for tomotherapy is another potential application.
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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 | 0.001 | 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) | 0.005 | 0.003 |
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".