Passive catheter visualization in magnetic resonance–guided endovascular therapy using multicycle projection dephasers
Bibliographic record
Abstract
PURPOSE: To improve upon the conventional projection dephaser (PD) method of background suppression and evaluate the use of multicycle projection dephasers to improve catheter conspicuity in background-suppressed MR images. MATERIALS AND METHODS: Passive visualization of endovascular catheters in MR images is compared using two background suppression techniques: 1) the conventional PD method and 2) the multicycle PD method. Contrast-filled 4-French (1.3 mm) catheters were imaged in homogeneous and heterogeneous phantoms, and in the common carotid artery of a canine using a modified spoiled gradient echo imaging sequence. We used catheter-to-background contrast (ranging from -100% to 100%) as the metric to compare background suppression techniques. RESULTS: In the homogeneous and heterogeneous phantoms, the contrast was -6.9% (catheter darker than background) and 15.0%, respectively, using the conventional PD method, and 50.6% and 44.0%, respectively, using the multicycle PD method. In the canine carotid artery, the contrast was -3.1% using the conventional PD method and 53.0% using the multicycle PD method. CONCLUSION: This work shows that multicycle projection dephasers improve catheter conspicuity over the conventional PD method. The multicycle PD method has potential for use in guiding endovascular procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".