MO‐F‐110‐04: Experimental Validation of a Novel Approach to Fast and Accurate Kilovoltage Dose Computation
Bibliographic record
Abstract
Purpose:To validate a novel hybrid method for the calculation of kilovoltage (kV) x‐ray dose. Methods: We performed experimental validation of a novel hybrid approach for calculating dose deposited by imaging beams (<150 kVp). The approach involves computing the primary photon component deterministically and scattered component stochastically, accounting for the real micro cross sections of the materials involved. Depth dose and profile measurements for the Varian® On‐Board Imaging® unit in the radiographic mode using a 125 kVp x‐ray beam (4.6 mm Al half‐value layer) were performed. The measurements were made using a Farmer‐type Capintec ion chamber (0.06cc) in homogeneous and heterogeneous phantoms. A phantom consisting of Certified Therapy‐Grade Solid Water® (Gammex 457) was used to measure the dose under homogeneous conditions. The heterogeneous phantom was constructed using lung‐ equivalent material (Gammex 455) and bone‐equivalent material (Gammex 450). Results: Measurements in the homogeneous phantom agreed with theoretical calculations within 2% for depth doses and within 2% of the central axis dose for profiles. Local agreement in highly attenuated regions such as depths >10 cm or outside the beam edge were about 10%. However, the dose differences in these regions were less than 2% of the maximum dose. Conclusions: This work provides experimental validation of our hybrid calculation method. The next step will be to test the algorithm using more complex phantoms, such as the anthropomorphic Rando® phantom, which mimics patient geometry as well as inhomogeneities. This is a crucial step in the validation of an independent tool to calculate patient dose from kV beams such as cone‐beam CT and brings us closer to our goal of calculating patient‐specific dose from imaging procedures.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".