Bibliographic record
Abstract
The clinical translation of MRI-guided cardiovascular catheterization has been limited by the unavailability of devices that are both visible and safe under MRI. In par-ticular, rigid metallic guidewires are essential for most catheterization procedures and are at risk of heating during MR imaging [1]. Here we present an MRI method that simultaneously improves the visualization of commercially available nitinol guidewires and mini-mizes RF induced heating. Methods RF-efficient gradient echo spiral imaging was chosen to minimize heating (8 interleaves, TE/TR = 0.86/10ms, flip angle = 10°). Through-slice dephasing generated a positive contrast “device image ” [2], exploiting local field inhomo-geneity such that the metallic guidewire appears hyperin-tense with background signal suppressed. An anatomical image and a device image were interleaved in alternating frames. Image processing (signal thresholding and selec-tion of elongated structures) was performed on the device image to isolate the guidewire signal from other sources of field inhomogeneity. Imaging was performed on a 1.5T MRI scanner (Aera, Siemens, Erlangen, Germany). MRI-guided left heart catheterization was performed in a pig using a 0.035 ” com-mercially available nitinol guidewire (Nitrex, Covidien, Ply-mouth, MN). The RF induced temperature rise at the tip of an insulated nitinol rod during MR imaging was mea-sured in the ASTM 2182 gel phantom using a fiberoptic temperature probe (OpSense, Quebec, Canada). Results A pair of anatomical and device images were generated with a temporal resolution of 160ms (80ms per image), or 6.25 frames/s. Image processing occurred in real-time and a color overlay of the device on the anatomy was dis-played to the operator for guidance during catheterization
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.310 | 0.137 |
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".