Evaluating small field dosimetry with the Acuros XB (AXB) and analytical anisotropic algorithm (AAA) dose calculation algorithms in the eclipse treatment planning system
Bibliographic record
Abstract
Abstract Background: An increasing number of external beam treatment modalities including intensity modulated radiation therapy, volumetric modulated arc therapy (VMAT) and stereotactic radiosurgery uses very small fields for treatment planning and delivery. However, there are major challenges in small photon field dosimetry, due to the partial occlusion of the direct photon beam source’s view from the measurement point, lack of lateral charged particle equilibrium, steep dose-rate gradient and volume averaging effect of the detector response and variation of the energy fluence in the lateral direction of the beam. Therefore, experimental measurements of dosimetric parameters such as percent depth doses (PDDs), beam profiles and relative output factors (ROFs) for small fields continue to be a challenge. Materials and Methods: In this study, we used a homogeneous water phantom and the heterogeneous anthropomorphic stereotactic end-to-end verification (STEEV) head phantom for all dose measurements and calculations. PDDs, lateral dose profiles and ROFs were calculated in the Eclipse Treatment Planning System version 13·6 using the Acuros XB (AXB) and the analytical anisotropic algorithms (AAAs) in a homogenous water phantom. Monte Carlo (MC) simulations and measurements using the Exradin W1 Scintillator were also accomplished for four photon energies: 6 MV, 6FFF, 10 MV and 10FFF. Two VMAT treatment plans were generated for two different targets: one located in the brain and the other in the neck (close to the trachea) in the head phantom (CIRS, Norfolk, VA, USA). A Varian Truebeam linear accelerator (Varian, Palo Alto, CA, USA) was used for all treatment deliveries. Calculated results with AXB and AAA were compared with MC simulations and measurements. Results: The average difference of PDDs between W1 Exradin Scintillator measurements and MC simulations, AAA and AXB algorithm calculations were 1·2, 2·4 and 3·2%, respectively, for all field sizes and energies. AXB and AAA showed differences in ROF of about 0·3 and 2·9%, respectively, compared with W1 Exradin Scintillator measured values. For the target located in the brain in the head phantom, the average dose difference between W1 Exradin Scintillator and the MC simulations, AAA and AXB were 0·2, 3·2 and 2·7%, respectively, for all field sizes. Similarly, for the target located in the neck, the respective dose differences were 3·8, 5·7 and 3·5%. Conclusion: In this study, we compared dosimetric parameters such as PDD, beam profile and ROFs in water phantom and isocenter point dose measurements in an anthropomorphic head phantom representing a patient. We observed that measurements using the W1 Exradin scintillator agreed well with MC simulations and can be used efficiently for dosimetric parameters such as PDDs and dose profiles and patient-specific quality assurance measurements for small fields. In both homogenous and heterogeneous media, the AXB algorithm dose prediction agrees well with MC and measurements and was found to be superior to the AAA algorithm.
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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 | 0.001 | 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 | 0.001 |
| 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".