Dual-Energy CT: Balance Between Iodine Attenuation and Artifact Reduction for the Evaluation of Head and Neck Cancer
Bibliographic record
Abstract
OBJECTIVE: Dual-energy computed tomography high energy virtual monochromatic images (VMIs) can reduce artifact but suppress iodine attenuation in enhancing tumor. We investigated this trade-off to identify VMI(s) that strike the best balance between iodine detection and artifact reduction. METHODS: The study was performed using an Alderson radiation therapy phantom. Different iodine solutions (based on estimated tumor iodine content in situ using dual-energy computed tomography material decomposition) and different dental fillings were investigated. Spectral attenuation curves and quality index (QI: 1/SD) were evaluated. RESULTS: The relationship between iodine attenuation and QI depends on artifact severity and iodine concentration. For low to average concentration solutions degraded by mild to moderate artifact, the iodine attenuation and QI curves crossed at 95 keV. CONCLUSIONS: High energy VMIs less than 100 keV can achieve modest artifact reduction while preserving sufficient iodine attenuation and could represent a useful additional reconstruction for evaluation of head and neck cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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".