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.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".