A fast scheme for renal microvascular perfusion functional imaging: Assessed by an imaging quality evaluation model
Bibliographic record
Abstract
PURPOSE: This study aimed to develop a fast scheme of multiparametric perfusion functional imaging (PFI) based on dynamic contrast-enhanced ultrasound (DCEUS) for assessing renal microvascular hemodynamics. METHOD: The flow process in the DCEUS-based PFI was modified step-by-step to improve its operational efficiency, which was validated through in vivo renal perfusion experiments. A multiparametric model with a comprehensive coefficient of imaging quality (CIQ) was then built on four terms of the average information entropy, contrast, gray, and noise coefficient of PFIs to evaluate the sacrifice of imaging quality during modifications of DCEUS-based PFI. RESULTS: = 0.73, P < 0.01). Compared with the raw scheme in the renal sagittal and coronal planes, the fast PFI scheme significantly improved its operational efficiency by 62.82 ± 1.07% (P < 0.01) and the nine PFIs simultaneously maintained a similar CIQ of 0.26 ± 0.06. CONCLUSIONS: The inhomogeneous hemodynamic distributions with a ring-like feature in the renal microvasculature were accurately and efficiently characterized by the fast PFI scheme. The fast PFI scheme can be applied for early diagnosis, follow-up evaluation and monitoring treatment of chronic kidney disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".