Real-time fast inverse dose optimization for image guided adaptive radiation therapy–Enhancements to fast inverse dose optimization (FIDO)
Bibliographic record
Abstract
A fast, accurate and stable optimization algorithm is very important for inverse planning of intensity-modulated radiation therapy (IMRT), and for implementing dose-adaptive radiotherapy in the future. Conventional numerical search algorithms with positive beam weight constraints generally require numerous iterations and may produce suboptimal dose results due to trapping in local minima regions of the objective function landscape. A direct solution of the inverse problem using conventional quadratic objective functions without positive beam constraints is more efficient but it will result in unrealistic negative beam weights. We review here a direct solution of the inverse problem that is efficient and does not yield unphysical negative beam weights. In fast inverse dose optimization (FIDO) method the objective function for the optimization of a large number of beamlets is reformulated such that the optimization problem is reducible to a linear set of equations. The optimal set of intensities is then found through a matrix inversion, and negative beamlet intensities are avoided without the need for externally imposed ad hoc conditions. In its original version [S. P. Goldman, J. Z. Chen, and J. J. Battista, in Proceedings of the XIVth International Conference on the Use of Computers in Radiation Therapy, 2004, pp. 112–115; S. P. Goldman, J. Z. Chen, and J. J. Battista, Med. Phys. 32, 3007 (2005)], FIDO was tested on single two-dimensional computed tomography (CT) slices with sharp KERMA beams without scatter, in order to establish a proof of concept which demonstrated that FIDO could be a viable method for the optimization of cancer treatment plans. In this paper we introduce the latest advancements in FIDO that now include not only its application to three-dimensional volumes irradiated by beams with full scatter but include as well a complete implementation of clinical dose-volume constraints including maximum and minimum dose as well as equivalent uniform dose constraints. The method has been integrated into a commercial treatment planning system (Pinnacle, Philips Medical Systems) for beta testing using clinical radiotherapy cases and standard dose constraints set by radiation oncologists. Our FIDO method consistently delivered excellent treatment plans comparable and often better than those obtained using standard optimization techniques that are considerably slower. By design, FIDO is guaranteed to find a global minimum and will achieve highly conformal and homogeneous dose distributions without the need for artificial internal contours that must be created in conventional IMRT optimization systems to help the search engines better control some beam entry directions and in this way avoid the creation of hot and cold spots. This method provides a fast, intuitive and robust technique that yields excellent results for the inverse planning of IMRT. FIDO’s ability to efficiently optimize very large numbers of beamlet weights also makes it an ideal tool for the optimization of helical tomotherapy. Future gains in speed will make it possible to use FIDO for instant treatment plan reoptimization using CT images produced at the radiotherapy machine, just prior to daily treatments of the patient.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".