Hepatic Perfusion Imaging Using Factor Analysis of Contrast Enhanced Ultrasound
Bibliographic record
Abstract
Contrast enhanced ultrasound imaging provides a real-time tool for evaluating vasculature in the liver. Primary liver cancer is known to be perfused exclusively by blood from the hepatic artery, whereas normal liver is also supplied by the portal vein. Visual separation of two different phases of enhancement from the independent feeding vessels is important for diagnosis but remains a challenge. This paper presents a method of using factor analysis for extracting distinct time-intensity curves. A key component to this extraction is the clustering of measured bolus curves and their projection onto a positivity domain to obtain nonnegative curves. This technique provides complementary images representing spatial loadings on each curve. As little as 1% of the data is required to contain unmixed signals to extract time-intensity curves that correlate well with true curves. A method of combining this information to display a regional hepatic perfusion image is proposed, and results are tested on a set of 10 patients. Region of interest analysis suggests it is possible to detect changes in the hepatic perfusion index of liver lesions relative to normal liver parenchyma using contrast ultrasound.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".