Contrast Echocardiography for Myocardial Perfusion Imaging Using Intravenous Agents: Progress and Promises
Bibliographic record
Abstract
AIMS: This article is a convenient overview to assist the interested echocardiographist towards acquiring his own experience in the field of myocardial perfusion imaging using intravenous contrast agents. This goal is now pursued in many centres, since contrast echo holds the advantages of cardiac ultrasound (non-invasiveness, high spatial and temporal resolution, wide availability, use of non-ionizing radiation), and because a variety of transpulmonary agents-together with a spectrum of imaging modalities-are becoming available. METHODS AND RESULTS: Many technical considerations need to be addressed for optimal myocardial perfusion imaging: characteristics of the contrast medium (air-filled or perfluorocarbon filled and/or encapsulated agents), modality of administration (bolus injection or continuous infusion) and interaction between microbubbles and ultrasound (dependency on power output). Moreover, intermittent harmonic imaging, intermittent harmonic power Doppler, pulse inversion and amplitude modulation imaging have all been developed to enhance microbubble detection over myocardial tissue. These new acquisition modalities also yield specific artifacts impacting on myocardial perfusion assessment. Finally, acute myocardial infarction and chronic ischaemic heart disease (at baseline and during stress) are the most studied clinical models for perfusion imaging with contrast echo, and are reviewed in this article. CONCLUSION: Perfusion imaging with intravenous contrast agents has never been as close to widespread clinical use as it is today, but many methodological issues remain unsettled before the wish of the contrast echocardiographist comes true: that is, a cheap, user-friendly and widely available technology that would disclose new information in echocardiography.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".