Segmentation and leaf sequencing for intensity modulated arc therapy
Bibliographic record
Abstract
A common method in generating intensity modulated radiation therapy (IMRT) plans consists of a three step process: an optimized fluence intensity map (IM) for each beam is generated via inverse planning, this IM is then segmented into discrete levels, and finally, the segmented map is translated into a set of MLC apertures via a leaf sequencing algorithm. To date, limited work has been done on this approach as it pertains to intensity modulated arc therapy (IMAT), specifically in regards to the latter two steps. There are two determining factors that separate IMAT segmentation and leaf sequencing from their IMRT equivalents: (1) the intrinsic 3D nature of the intensity maps (standard 2D maps plus the angular component), and (2) that the dynamic multileaf collimator (MLC) constraints be met using a minimum number of arcs. In this work, we illustrate a technique to create an IMAT plan that replicates Tomotherapy deliveries by applying IMAT specific segmentation and leaf-sequencing algorithms to Tomotherapy output sinograms. We propose and compare two alternative segmentation techniques, a clustering method, and a bottom-up segmentation method (BUS). We also introduce a novel IMAT leaf-sequencing algorithm that explicitly takes leaf movement constraints into consideration. These algorithms were tested with 51 angular projections of the output leaf-open sinograms generated on the Hi-ART II treatment planning system (Tomotherapy Inc.). We present two geometric phantoms and 2 clinical scenarios as sample test cases. In each case 12 IMAT plans were created, ranging from 2 to 7 intensity levels. Half were generated using the BUS segmentation and half with the clustering method. We report on the number of arcs produced as well as differences between Tomotherapy output sinograms and segmented IMAT intensity maps. For each case one plan for each segmentation method is chosen for full Monte Carlo dose calculation (NumeriX LLC) and dose volume histograms (DVH) are calculated. In all cases, the BUS method outperformed the clustering, method. We recommend using the BUS algorithm and discuss potential improvements to the clustering algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".