Quantitative imaging performance of MARS spectral photon‐counting CT for radiotherapy
Bibliographic record
Abstract
PURPOSE: To evaluate the quantitative imaging performance of a spectral photon-counting computed tomography (SPCCT) scanner for radiotherapy applications. An experimental comparison of the quantitative performance of a Siemens dual-energy CT (DECT) and a MARS SPCCT scanner is performed to estimate physical properties relevant to radiotherapy of human substitute materials and contrast agent solutions. In human substitute materials, the accuracy of quantities relevant to photon therapy, proton therapy, and Monte-Carlo simulations, such as the electron density, proton stopping power, and elemental composition is evaluated. For contrast agent solutions, the accuracy of the contrast agent concentrations and the virtual non-contrast (VNC) electron density is evaluated. METHODS: Human tissue substitute phantoms (Gammex 467 and 472) as well as diluted solutions of contrast agents (iodine and gadolinium based) are scanned with two commercial systems: a Siemens dual-source CT (SOMATOM Definition Flash, Siemens Healthineers, Forchheim, Germany) and a MARS spectral photon-counting micro-CT (MARS V5.2, MARS Bioimaging Ltd., Christchurch, New Zealand). Material decomposition is performed in a maximum a posteriori framework with an optimized material basis tailored to characterize either human substitute materials or contrast agents in the context of experimental multi-energy CT data. RESULTS: The root-mean-square error (RMSE) of the electron density calculated over all Gammex inserts is reduced from 1.09 to 0.89% when going from DECT to SPCCT. For the proton stopping power, the RMSE is reduced from 1.92 to 0.89%. Elemental mass fractions of hydrogen, carbon, nitrogen, oxygen, and calcium are more accurately estimated with the MARS scanner. The RMSE on the iodine-based contrast agents concentration is reduced from 0.27 to 0.12 mg/mL with SPCCT, and the VNC electron density from 0.40 to 0.22%. CONCLUSION: In the present phantom study, a MARS photon-counting scanner provides superior accuracy compared to a Siemens SOMATOM Definition Flash DECT scanner to quantify physical parameters relevant to radiotherapy. This work experimentally demonstrates the benefits of using more energies to characterize human tissue equivalent materials. This highlights the potential of SPCCT for particle therapy, where more accurate tissue characterization is needed, as well as for Monte-Carlo based planning, which requires accurate elemental mass fractions.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".