Impact of Field Strength in Clinical Cardiac Magnetic Resonance Imaging
Bibliographic record
Abstract
ABSTRACT: Cardiac magnetic resonance imaging (MRI) is widely applied for the noninvasive assessment of cardiac structure and function, and for tissue characterization. For more than 2 decades, 1.5 T has been considered the field strength of choice for cardiac MRI. Although the number of 3-T systems significantly increased in the past 10 years and numerous new developments were made, challenges seem to remain that hamper a widespread clinical use of 3-T MR systems for cardiac applications. As the number of clinical cardiac applications is increasing, with each having their own benefits at both field strengths, no "holy grail" field strength exists for cardiac MRI that one should ideally use. This review describes the physical differences between 1.5 and 3 T, as well as the effect of these differences on major (routine) cardiac MRI applications, including functional imaging, edema imaging, late gadolinium enhancement, first-pass stress perfusion, myocardial mapping, and phase contrast flow imaging. For each application, the advantages and limitations at both 1.5 and 3 T are discussed. Solutions and alternatives are provided to overcome potential limitations. Finally, we briefly elaborate on the potential use of alternative field strengths (ie, below 1.5 T and above 3 T) for cardiac MRI and conclude with field strength recommendations for the future of cardiac MRI.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".