Bibliographic record
Abstract
Dual‐energy CT technology is becoming increasingly available to the medical imaging community. In addition, several models of CT simulators sold for use in radiation therapy departments now feature dual‐energy technology. The images provided by dual‐energy CT scanners add new information to the radiation treatment planning process; multiple spectral components can be used to separate and identify material composition as well as generate virtual monoenergetic images. In turn, this information could be used to investigate pathologic processes, separate the properties of contrast agents from soft tissues, assess tissue response to therapy, and other applications of therapeutic interest. Additionally, the decomposition of materials in images could directly integrate with and impact the accuracy of dose calculation algorithms. This symposium will explore methods of generating dual‐energy CT images, spectral and image analysis algorithms, current and future applications of interest in oncologic imaging, and unique considerations when using dualenergy CT images in the radiation treatment planning process. Learning Objectives: Understand acquisition techniques and processing algorithms used in dual‐energy CT imaging. Discover current and future applications of dual‐energy CT images in the radiological oncology setting. Introduce some of the unique considerations and QA requirements when integrating a dual‐energy CT scanner into the radiation therapy environment. N. Pelc: GE Healthcare, Philips Healthcare, Sony; U. Schoepf: Consultant for and / or research support from Astellas, Bayer,;Bracco, GE, Guerbet, Medrad, Siemens
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".