Sci—Thur AM: YIS ‐ 09: Validation of a General Empirically‐Based Beam Model for kV X‐ray Sources
Bibliographic record
Abstract
Purpose: To present an empirically‐based beam model for computing dose deposited by kilovoltage (kV) x‐rays and validate it for radiographic, CT, CBCT, superficial, and orthovoltage kV sources. Method and Materials: We modeled a wide variety of imaging (radiographic, CT, CBCT) and therapeutic (superficial, orthovoltage) kV x‐ray sources. The model characterizes spatial variations of the fluence and spectrum independently. The spectrum is derived by matching measured values of the half value layer (HVL) and nominal peak potential (kVp) to computationally‐derived spectra while the fluence is derived from in‐air relative dose measurements. This model relies only on empirical values and requires no knowledge of proprietary source specifications or other theoretical aspects of the kV x‐ray source. To validate the model, we compared measured doses to values computed using our previously validated in‐house kV dose computation software, kVDoseCalc. The dose was measured in homogeneous and anthropomorphic phantoms using ionization chambers and LiF thermoluminescent detectors (TLDs), respectively. Results: The maximum difference between measured and computed dose measurements was within 2.6%, 3.6%, 2.0%, 4.8%, and 4.0% for the modeled radiographic, CT, CBCT, superficial, and the orthovoltage sources, respectively. In the anthropomorphic phantom, the computed CBCT dose generally agreed with TLD measurements, with an average difference and standard deviation ranging from 2.4 ± 6.0% to 5.7 ± 10.3% depending on the imaging technique. Most (42/62) measured TLD doses were within 10% of computed values. Conclusions: The proposed model can be used to accurately characterize a wide variety of kV x‐ray sources using only empirical values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".