Sci-PM Thurs - 05: Improving background suppression in magnetic resonance-guided endovascular therapy
Bibliographic record
Abstract
Vascular disease is a leading cause of death in Canada. Endovascular therapy represents a minimally invasive means of treating this disease. The current clinical standard for endovascular treatment uses x-ray imaging as the modality to visualize the vasculature and devices introduced into the vascular system. Magnetic resonance (MR) imaging is a better modality in terms of patient safety and has potential for use in clinical endovascular therapy. Before that can happen, we must show that we can reliably visualize and track catheters within a slice of tissue under MR guidance. One way of increasing catheter conspicuity is the projection dephaser (PD) method of background suppression.(Dixon et al., MRM, 1986) We propose another approach, in which multiple phase cycles are applied over the slice thickness, such that upon projection into one plane, the background tissue signal adds destructively while the catheter signal is minimally affected. In a 3 Tesla MR scanner, we imaged a 4 French catheter (1.3 mm) in a pork chop phantom with a fast spoiled gradient echo sequence (repetition time/echo time/ flip angle/ slice thickness = 7.5 ms/ 3.6 ms/ 20°/ 70 mm). Visual analysis of acquired images shows that catheter conspicuity is significantly improved over the PD method. Quantitatively, catheter contrast, , in the PD-suppressed image is 15% (Sc = catheter signal, Sb = background signal). With 35 phase cycles over the slice thickness, C is increased to 44%. These results show that this background suppression technique has potential for use in MR-guided endovascular procedures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".