Influence of intravenous contrast agent on dose calculation in proton therapy using dual energy CT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it