Evolution of 3D X-Ray Dose Computation Algorithms
Bibliographic record
Abstract
Abstract Radiation treatment planning of individual cancer patients relies on the accurate computation of dose distributions in irradiated tissue. Inaccurate dose maps have the potential to mislead clinical decision-making and compromise the balance between effective tumour control and side effects in surrounding normal tissue. In the context of this conference, 3D dosimetry is important for the experimental validation of computed dose distributions. Dose computation methods for external beams of high energy x rays have evolved over the past decade with computer simulation models more closely aligned with the fundamental physics of x-ray scattering and absorption in heterogeneous tissue. In this article, we first present a historical review from a Canadian perspective, followed by an introductory intuitive description of contemporary algorithms used in clinical treatment planning: (1) Convolution-superposition algorithm fundamentally based on the Green’s function method; (2) Stochastic Monte Carlo simulation of x-ray interactions in tissue, and (3) Deterministic numerical solution of a system of Boltzmann transport equations. In principle, all these methods solve the same problem of predicting x-ray scattering and absorption in heterogeneous tissue. However, the mathematical tools differ in their approach and approximations to achieve sufficient speed for routine clinical application. In the conclusion of this article, the evolution of 3D x-ray dose computation is summarized, in terms of accuracy and computational speed.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".