Influence of intravenous contrast agent on dose calculation in proton therapy using dual energy CT
Bibliographic record
Abstract
The purpose of this study is to evaluate the effect of an intravenous (IV) contrast agent on proton therapy dose calculation using dual-energy computed tomography (DECT). Two DECT methods are considered. The first one, [Formula: see text], attempts to accurately predict the proton stopping powers relative to water (SPR) of contrast enhanced (CE) DECT images, while the second generates a virtual non-contrast (VNC) volume that can be processed as a native non-contrast (NC) one. Both methods are compared against single-energy computed tomography (SECT). The accuracy of SPR predicted for different concentrations of IV contrast diluted in water is first evaluated using simulated data. Results then are validated in an experimental set-up comparing SPR predictions for both NC and CE images to measurements made with a multi-layer ionisation chamber (MLIC). Finally, the impact of IV contrast on dose calculation using both SECT and DECT is evaluated for one liver and one head and neck patient. Using simulated data, DECT is shown to be less sensitive to the presence of IV contrast than SECT, although the performance of the [Formula: see text] method is sensitive to the level of beam hardening considered. For different concentrations of IV contrast diluted in water, experimental MLIC measurement of SPR agrees with DECT predictions within 3% while SECT introduce errors above 20%. This error in the SPR value results in a range error of up to 3.2 mm (2.6%) for proton beams calculated on SECT CE patient images. The error is reduced below 1 mm using DECT with the [Formula: see text] and VNC methods. Globally, it is observed that the influence of IV contrast on proton therapy dose calculation is mitigated using DECT over SECT. In patient anatomies, the VNC approach provides the best agreement with the reference dose distribution.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".