A simulation‐based analysis of the potential of compressed sensing for accelerating passive mr catheter visualization in endovascular therapy
Bibliographic record
Abstract
Passive MRI is a promising approach to visualize catheters in guiding and monitoring endovascular intervention and may offer several clinical advantages over the current x-ray fluoroscopy "gold standard." Endovascular MRI has limitations, however, such as difficulty in visualizing catheters and insufficient temporal resolution. The multicycle projection dephaser method is a background signal suppression technique that improves the conspicuity of passive catheters by generating a sparse (i.e., catheter only) image. One approach to improve the temporal resolution is to undersample the k-space and then apply nonlinear methods, such as compressed sensing, to reconstruct the MR images. This feasibility study investigates the potential synergies between multicycle projection dephaser and compressed sensing reconstruction for real-time passive catheter tracking. The multicycle projection dephaser method efficiently suppressed the background signal, and compressed sensing allowed MR images to be reconstructed with superior catheter conspicuity and spatial resolution when compared to the more conventional zero-filling reconstruction approach. Moreover, compressed sensing allowed the shortening of total acquisition time (by up to 32 times) by vastly undersampling the k-space while simultaneously preserving spatial resolution and catheter conspicuity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".