Spectral virtual non‐contrast imaging assisted by artificial intelligence segmentation
Bibliographic record
Abstract
Abstract Purpose The purpose of this study is to adapt a Bayesian dual‐virtual non‐contrast (VNC) method by integrating prior anatomical knowledge from AI‐based multi‐organ segmentation and to generalize it for spectral photon‐counting CT (PCCT) with an arbitrary number of energy channels. Methods A previously proposed Bayesian VNC method is reformulated for any number of energies and adapted for integration with AI segmentation. TotalSegmentator, an open‐access whole‐body AI segmentation model, is used to provide spatial priors. The method is applied to simulated contrast‐enhanced dual‐energy CT (DECT) and PCCT datasets from eight virtual patients, with and without AI segmentation. Key radiotherapy‐relevant parameters such as electron density () and proton stopping power ratio (SPR) are estimated and compared to ground truth values. Additional results are obtained for non‐contrast scans by setting contrast agent uptake to zero. Results AI‐based segmentation improved the accuracy of parameter estimation for both DECT and PCCT, with a more pronounced effect for PCCT. The combination of high spectral resolution and anatomical priors led to reduced RMS errors in SPR and . Mean absolute water‐equivalent path length (WEPL) errors confirmed the superiority of segmentation‐assisted PCCT over other methods. Conclusion This proof of concept demonstrates a flexible, AI‐assisted Bayesian framework for extracting quantitative information from contrast‐enhanced spectral CT. By integrating AI segmentation and generalizing to PCCT, the method shows improved tissue characterization, suggesting the value of AI in extracting quantitative information beyond DECT. Further validation on clinical datasets is needed. Background Quantitative VNC methods offer the potential to extract radiotherapy‐related parameters from contrast‐enhanced spectral CT without the need for additional non‐contrast imaging. However, the inherently ill‐posed nature of tissue characterization from limited spectral data remains a major limitation, which requires advanced techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".