MR feature tracking in patients with MRI‐conditional pacing systems: The impact of pacing
Bibliographic record
Abstract
PURPOSE: To develop feature tracking (FT) software to perform strain analysis on conventional (nontagged) cardiac magnetic resonance imaging (MRI) function images. With the advent of MRI-conditional pacemaker systems, effects of cardiac pacing on myocardial strain can be studied using MR. In this study the impact of pacing on left ventricular (LV) strain was investigated using MR-FT in patients with an MRI-conditional cardiac implantable electronic device (CIED). MATERIALS AND METHODS: FT was performed on 32 1.5T MR studies (16 patients with an MRI-conditional CIED and 16 control patients with normal scans). Short- and long-axis steady state free precession (SSFP) cines were used for the FT analysis. Strain was assessed using CVI(42) software (Circle Cardiovascular Imaging, Alberta, Canada). In addition, the intra- and interobserver variability was determined using the intraclass correlation coefficient. RESULTS: Of the 16 patients with an MRI-conditional CIED, five patients were paced during the MRI exam. Despite the occasional presence of susceptibility artifacts induced by the CIED, radial, circumferential, and longitudinal strain parameters could be derived for all patients. Peak radial strain and peak circumferential strain were reduced during pacing when compared to the control group; for radial strain: 20.1 ± 4.7% vs. 33.1 ± 6.9%, P < 0.001, and for circumferential strain -7.5 ± 3.5% vs. -14.9 ± 3.2%, P < 0.05. Peak strain parameters were reproducible on an intra- and interobserver level. CONCLUSION: MR-FT is feasible in patients with an MRI-conditional CIED and can be used to quantify regional wall motion. J. MAGN. RESON. IMAGING 2016;44:964-971.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".