SU‐F‐18C‐04: A Combination of Monoenergetic Reconstruction and Stoichiometric Calibration for Tissue Characterization Using Dual Energy Computed Tomography
Bibliographic record
Abstract
Purpose: Dual energy computed tomography (DECT) pre‐reconstruction methods require the prior knowledge of the X‐ray source spectrum to allow extracting physical parameters needed for radiation therapy dose calculation, such as electron density (ED) and the effective atomic number (EAN). While DECT stoichiometric calibration may provide reliable performance for typical radiation therapy clinical conditions, it is yet to be adapted to prereconstruction methods. The presence of noise and inaccurate spectrum description may lead to systematic errors and artifacts which compromise the accuracy of treatment planning. Methods: A new technique is investigated which consists in applying a DECT stoichiometric calibration method to a set of monoenergetic images obtained with a DECT prereconstruction method. To evaluate the performance of this extended method, a simulation environment is developed to generate DECT scans under well controlled conditions, to reconstruct monoenergetic images of a tissue‐equivalent phantom from transformed sinograms and to extract ED and EAN maps using a DECT formalism. Result: Under simulated clinical conditions, the accuracy in determining ED with the extended method versus a pre‐reconstruction method alone is shown to be better than 0.35% versus 0.5%, respectively. In the presence of a realistic noise level, EAN determination presents a relative mean error that drops from 2.5% to 0.5% once the calibration is applied. Considering a spectrum alteration by a 1 mm Cu layer, EAN errors are up to 30% for the pre‐reconstruction method alone versus less than 3% for the extended method. Conclusion: This study shows that combining pre‐reconstruction DECT methods with a stoichiometric calibration considerably improves the accuracy and reliability of tissue characterization for radiation therapy in a clinical context. The presented method could potentially be adapted as gold standard for dose calculation methods based on DECT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".