<i>In‐vitro</i>evaluation of accuracy of dynamic contrast‐enhanced ultrasound (<scp>DCEUS</scp>)‐based parametric perfusion imaging with respiratory motion‐compensation
Bibliographic record
Abstract
PURPOSE: The accuracy of multi-parametric perfusion imaging (PPI) based on dynamic contrast-enhanced ultrasound is disturbed by the respiratory motion in some cases, especially during characterizing hemodynamic features of abdominal tumor angiogenesis. This study aimed to effectively remove those disturbances on PPI and evaluate its accuracy. METHOD: The respiratory motion-compensation (rMoCo) strategy in PPI was modified by employing non-negative matrix factorization combined with phase-by-phase compensation. According to the known and controllable ground truths in in-vitro perfusion experiments, the accuracy of the modified rMoCo strategy was further evaluated from multiple perspectives in a simulated dual-vessel flow phantom. RESULTS: Compared with that of PPIs without rMoCo, the mean correlation coefficient between six PPIs with rMoCo and the corresponding static PPIs was up to 0.98 ± 0.01 and improved by 0.17 ± 0.04 (P < 0.05). The estimated error of vascular diameter decreased from 87.85% (P < 0.05) to 7.25% (P < 0.05) after rMoCo. PPIs with rMoCo were significantly consistent with static PPIs without respiratory motion disturbances. CONCLUSIONS: These quantitative results illustrated the disturbances induced by respiratory motion were effectively removed and the accuracy of PPIs was significantly improved. The partial parabolic and bimodal hemodynamic characteristics and the anatomical structures and sizes were accurately quantified and depicted by PPIs with rMoCo. The modified method can benefit physicians in providing accurate diagnoses and in developing appropriate therapeutic strategies for abdominal diseases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".