Sci‐Fri PM: Planning‐11: Selection and optimization of angiographic roadmap images for magnetic resonance guided catheter tracking
Bibliographic record
Abstract
As endovascular magnetic resonance (MR) techniques for device tracking and guidance move closer to demonstrating clinical feasibility, more investigation in the generation and optimization of vascular roadmap images is need to achieve the full benefit of MR-guided procedure. MR angiographic roadmap imaging requires high signal-to-noise (SNR), good vascular-to-background contrast and short acquisition time. These requirements not only qualify the appropriate roadmaps for therapy, but also guide in the optimization of their acquisition parameters. We hypothesize that among the well established MR angiographic techniques, low-resolution phase-contrast (PC) images would prove satisfactory for vascular roadmap imaging. To verify this, four potential MR angiography techniques, specifically, time-of-flight, contrast-enhanced, phase-contrast and black-blood angiography, were explored for roadmap imaging using a canine model on a 3 T MR scanner. PC angiography was specifically performed to evaluate impact of key parameters on the SNR efficiency and vascular-to-background contrast efficiency in order to optimize the sequence for therapeutic use. Data were collected from five canines. Phase-contrast angiography was found to be most suitable for generating vascular roadmap for MR-guided endovascular therapy. It was also found that small acquisition matrix and large FOV produced satisfactory roadmap images provided that the size of the vessel of interest was more than a few times the in-plane pixel dimension. Also, reducing the phase encoding steps had minimal effect on vessel oriented parallel to the phase encode direction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".