Application of direct virtual coil to dynamic contrast‐enhanced MRI and MR angiography with data‐driven parallel imaging
Bibliographic record
Abstract
PURPOSE: To demonstrate the feasibility of direct virtual coil (DVC) in the setting of 4D dynamic imaging used in multiple clinical applications. THEORY AND METHODS: Three dynamic imaging applications were chosen: pulmonary perfusion, liver perfusion, and peripheral MR angiography (MRA), with 18, 11, and 10 subjects, respectively. After view-sharing, the k-space data were reconstructed twice: once with channel-by-channel (CBC) followed by sum-of-squares coil combination and once with DVC. Images reconstructed using CBC and DVC were compared and scored based on overall image quality by two experienced radiologists using a five-point scale. RESULTS: The CBC and DVC showed similar image quality in image domain. Time course measurements also showed good agreement in the temporal domain. CBC and DVC images were scored as equivalent for all pulmonary perfusion cases, all liver perfusion cases, and four of the 10 peripheral MRA cases. For the remaining six peripheral MRA cases, DVC were scored as slightly better (not clinically significant) than the CBC images by Radiologist A and as equivalent by Radiologist B. CONCLUSION: For dynamic contrast-enhanced MR applications, it is clinically feasible to reduce image reconstruction time while maintaining image quality and time course measurement using the DVC technique.
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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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".