SU‐F‐T‐398: Improving Radiotherapy Treatment Planning Using Dual Energy Computed Tomography Based Tissue Characterization
Bibliographic record
Abstract
Purpose: Both kVp settings and geometric distribution of various materials lead to significant change of the HU values, showing the largest discrepancy for high‐Z materials and for the lowest CT scanning kVp setting. On the other hand, the dose distributions around low‐energy brachytherapy sources are highly dependent on the architecture and composition of tissue heterogeneities in and around the implant. Both measurements and Monte Carlo calculations show that improper tissue characterization may lead to calculated dose errors of 90% for low energy and around 10% for higher energy photons. We investigated the ability of dual‐energy CT (DECT) to characterize more accurately tissue equivalent materials. Methods: We used the RMI‐467 heterogeneity phantom scanned in DECT mode with 3 different set‐ups: first, we placed high electron density (ED) plugs within the outer ring of the phantom; then we arranged high ED plugs within the inner ring; and finally ED plugs were randomly distributed. All three setups were scanned with the same DECT technique using a single‐source DECT scanner with fast kVp switching (Discovery CT750HD; GE Healthcare). Images were transferred to a GE Advantage workstation for DECT analysis. Spectral Hounsfield unit curves (SHUACs) were then generated from 50 to 140‐keV, in 10‐keV increments, for each plug. Results: The dynamic range of Hounsfield units shrinks with increased photon energy as the attenuation coefficients decrease. Our results show that the spread of HUs for the three different geometrical setups is the smallest at 80 keV. Furthermore, among all the energies and all materials presented, the largest difference appears at high Z tissue equivalent plugs. Conclusion: Our results suggest that dose calculations at both megavoltage and low photon energies could benefit in the vicinity of bony structures if the 80 keV reconstructed monochromatic CT image is used with the DECT protocol utilized in this work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".