Quantitative functional imaging with CT perfusion: technical considerations, kinetic modeling, and applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CT perfusion (CTP)-derived quantitative maps of hemodynamic parameters have found important clinical applications in stroke, cancer, and cardiovascular disease. Blood flow, blood volume, transit time, and other perfusion parameters are sensitive markers of pathophysiology with impaired perfusion. This review summarizes the basic principles of CTP including image acquisition, tracer kinetic modeling, deconvolution algorithms, and diagnostic interpretation. The focus is on practical and theoretical considerations for accurate quantitative parametric imaging. Recommended CTP scan parameters to maintain CT number accuracy and optimize radiation dose versus image noise are first reviewed. Tracer kinetic models, which describe how injected contrast material is distributed between blood and the tissue microenvironment by perfusion and bidirectional passive exchange, are then derived. Deconvolution algorithms to solve for hemodynamic parameters of kinetic models are discussed and their quantitative accuracy benchmarked. The applications and diagnostic interpretation of CTP in stroke, cancer, and cardiovascular disease are summarized. Finally, we conclude with a discussion of future directions for CTP research, including radiation dose reduction, new opportunities with novel CT hardware, and emerging diagnostic applications.
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.
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 it