Does kV–MV dual-energy computed tomography have an advantage in determining proton stopping power ratios in patients?
Bibliographic record
Abstract
Conventional kilovoltage (kV) x-ray-based dual-energy CT (DECT) imaging using two different x-ray energy spectra is sensitive to image noise and beam hardening effects. The purpose of this study was to evaluate the theoretical advantage of the DECT method for determining proton stopping power ratios (SPRs) using a combination of kV and megavoltage (MV) x-ray energies. We investigated three representative x-ray energy pairs: 100 and 140 kVp comprised the kV-kV pair, 100 kVp and 1 MV comprised the kV-MV pair, and two 1 MV x-ray beams-one with and one without external filtration-comprised the MV-MV pair. The SPRs of 34 human tissues were determined using the DECT method with these three x-ray energy pairs. Small perturbations were introduced into the CT numbers and x-ray spectra used for the DECT calculation to simulate the effects of random noise and beam hardening. An error propagation analysis was performed on the DECT calculation algorithm to investigate the propagation of CT number uncertainty to final SPR estimation and to suggest the best x-ray energy combination. We found that the DECT method using each of the three beam pairs achieved similar accuracy in determining the SPRs of human tissues in ideal conditions. However, when CT number uncertainties and artifacts such as imaging noise and beam hardening effects were considered, the kV-MV DECT improved the accuracy of SPR estimation substantially over the kV-kV or MV-MV DECT methods. Furthermore, our error propagation analysis showed that the combination of 100 kVp and 1 MV beams was close to the optimal selection when using the DECT method to determine SPRs. Overall, the kV-MV combination makes the DECT method more robust in resolving the effective atomic numbers for biological tissues than the traditional kV-kV DECT method.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".