Bibliographic record
Abstract
The metastatic spreading ability of cancer heavily depends on microcirculation, requiring the formation of new blood vessels in the vascular network, or angiogenesis. Indirect measurement of angiogenesis can be performed by computed tomography (CT) perfusion imaging, where blood flow is observed using dynamic contrast-enhanced techniques. However, contrast-enhancement in tissues can be poor due to inadequate CT calibration, leading to an increase in radiation dose to the patient. It is hypothesized that the use of dual-energy CT will provide a more robust basis for calibration of contrast-enhanced imaging without increasing dose. The purpose of this study is to improve the sensitivity and accuracy of perfusion imaging by using atomic number extraction of Iodine contrast agent concentration from dual-energy datasets. A stoichiometric calibration for CT was done with the effective atomic number and relative electron density extracted in MATLAB, resulting in a wider dynamic range of effective atomic number per mgI/mL. Dual-energy CT as a tool for calibrating perfusion imaging shows promise of increasing the range and signal to noise ratio of contrast-enhanced CT imaging. Future work will extend the study from a static to dynamic quantification to explore dual-energy CT for perfusion imaging, with the goals of developing a robust stoichiometric calibration for quality assurance purposes, and further insights on tumour angiogenesis and cancer metastasis.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".