SU‐E‐J‐182: Validation of Two Mathematical Formalisms for Tissue Characterization in Dual Energy Computed Tomography
Bibliographic record
Abstract
Purpose: While dose calculations are typically performed using a simplistic correspondence of HU to electron density (ED), recent developments in DECT for radiotherapy could provide significant improvements in characterizing human tissues for such purpose. We aim to compare and validate two DECT mathematical formalisms and evaluate their accuracy in terms of ED and effective atomic number (Z) for radiotherapy applications. Methods: Two cylindrical phantoms (Catphan 504 and Gammex 467) containing tissue substitutes are scanned with a Philips Gemini GXL CT at 90, 120 and 140 kV. Two mathematical formalisms are developed and implemented using MATLAB, allowing the extraction of ED and effective Z maps of various materials, given a pair of CT images taken at two distinctive energies. The first formalism is based on a parameterization of XCOM cross sections and uses generic photon spectra provided by the manufacturer. The second formalism is based on a stoichiometric calibration of HU and uses experimental data and the substitutes' composition. A novel definition of effective Z is developed for both formalisms. Results: With the 90–120 kV energy pair, the extraction of relative ED of the Catphan materials leads to a maximum relative error of 5% for the XCOM‐based formalism and 2% for the stoichiometric‐based formalism. In the instance of the Gammex materials, higher density materials, as bones, present errors up to 31% and 15% respectively. Conclusion: While the stoichiometric‐based formalism demonstrates a clear advantage over the XCOM‐based formalism in the analysis of CT data acquired clinically, both yield reasonable accuracy for low‐Z elements materials. Conversely, the results for high‐Z materials are negatively affected by discontinuities present in photoelectric effect cross sections. An advanced formalism, which would precisely parameterize this effect, is expected to yield improvements in accuracy and lead the way to a successful implantation of DECT in radiotherapy treatment planning.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".