Quantification of renal perfusion: Comparison of arterial spin labeling and dynamic contrast‐enhanced MRI
Bibliographic record
Abstract
PURPOSE: To provide the first comparison of absolute renal perfusion obtained by arterial spin labeling (ASL) and separable compartment modeling of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). Moreover, we provide the first application of the dual bolus approach to quantitative DCE-MRI perfusion measurements in the kidney. MATERIALS AND METHODS: Consecutive ASL and DCE-MRI acquisitions were performed on six rabbits on a 1.5 T MRI system. Gadolinium (Gd)-DTPA was administered in two separate injections to decouple measurement of the arterial input function and tissue uptake curves. For DCE perfusion, pixel-wise and mean cortex region-of-interest tissue curves were fit to a separable compartment model. RESULTS: Absolute renal cortex perfusion estimates obtained by DCE and ASL were in close agreement: 3.28 ± 0.59 mL/g/min (ASL), 2.98 ± 0.60 mL/g/min (DCE), and 3.57 ± 0.96 mL/g/min (pixel-wise DCE). Renal medulla perfusion was 1.53 ± 0.35 mL/g/min (ASL) but was not adequately described by the separable compartment model. CONCLUSION: ASL and DCE-MRI provided similar measures of absolute perfusion in the renal cortex, offering both noncontrast and contrast-based alternatives to improve current renal MRI assessment of kidney function.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".