Poster — Wed Eve—14: Real‐Time MR Imaging for Angioplasty
Bibliographic record
Abstract
Angioplasty has been demonstrated as an effective treatment for cardiovascular diseases such as carotid stenosis, having similar patient outcomes to the once dominant endarectomy technique. Angioplasty is an attractive choice, as it is much less invasive. However, angioplasty procedures are hinged on the guidance of catheters through the vascular system, and imaging is required for this process. X‐ray is almost always used for these types of interventions, but has several noted drawbacks, including the exposure of ionizing radiation to both patients and staff. Magnetic resonance (MR) imaging has been used in previous experiments at different centres and overcomes some of the problems associated with X‐ray imaging. Here, we propose a real‐time imaging system, for use in catheter guiding applications, and look at parameters and techniques that will increase the overall frame rate displayed to an in‐room monitor. By modifying a fast gradient recalled echo (FGRE) sequence, and ported data directly to an image reconstruction station, implemented on an iMac computer, images are reconstructed and display in real time. By using algorithms such as variable rate k‐space acquisition (varking), multi‐phase array coils, reducing the number of phase‐encode lines, and reducing the analog to digital converter (ADC) sampling rate, frame rate was improved from ∼1 Hz to ∼5Hz. Analysis of images, pre‐ and post‐optimization, yield comparable quality by inspection, and an improved SNR from 45 to 160. This system has been designed to perform MR angioplasty procedures, which will the next step in our research project using animal models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.121 | 0.049 |
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".