Vega library for processing DICOM data required in Monte Carlo verification of radiotherapy treatment plans
Bibliographic record
Abstract
Monte Carlo (MC) methods provide the most accurate to-date dose calculations in heterogeneous media and complex geometries, and this spawns increasing interest in incorporating MC calculations into treatment planning quality assurance process. This involves MC dose calculations for clinically produced treatment plans. To perform these calculations, a number of treatment plan parameters specifying radiation beam and patient geometries need to be transferred to MC codes, such as BEAMnrc and DOSXYZnrc. Extracting these parameters from DICOM files is not a trivial task, one that has previously been performed mostly using Matlab-based software. This paper describes the DICOM tags that contain information required for MC modeling of conformal and IMRT plans, and reports the development of an in-house DICOM interface, through a library (named Vega) of platform-independent, object-oriented C++ codes. The Vega library is small and succinct, offering just the fundamental functions for reading/modifying/writing DICOM files in a C++ program. The library, however, is flexible enough to extract all MC required data from DICOM files, and write MC produced dose distributions into DICOM files that can then be processed in a treatment planning system environment. The library can be made available upon request to the authors.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.121 | 0.056 |
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".