Bibliographic record
Abstract
Abstract Dual-energy computed tomography (DECT) is an advanced form of computed tomography (CT), in which image acquisition is performed at two different energy spectra, instead of a single-energy spectrum using conventional single-energy CT (SECT). This enables the creation of different reconstructions and quantitative spectral tissue analysis beyond what is possible with SECT. In adults, there are increasing clinical applications of DECT for all organ systems, including neuroimaging and head and neck imaging. However, there are relatively few studies evaluating applications of DECT for pediatric imaging and little to none in neuroimaging or head and neck imaging. The purpose of this article is to provide an overview and familiarize the readers with DECT. This article will review the fundamental principles behind DECT, including different DECT acquisition systems and principles of DECT material characterization. This will be followed by a review of potential applications of DECT, many based on imaging the head and neck. The objectives are to familiarize the readers with this exciting technology and hopefully serve as a primer for investigations and applications of DECT for pediatric neuro and head and neck imaging.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".