Combined imaging of myocardial scar and fat using AI-guided dual bright- and black-blood late gadolinium enhancement

Clicks: 2
ID: 338197
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #72 of 72 articles by views in European Heart Journal - Imaging Methods and Practice

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Abstract Aims Characterization of myocardial scar and fat is clinically important for diagnosis, risk stratification, and procedural planning in cardiomyopathies. While black-blood late gadolinium enhancement (LGE) improves scar conspicuity, fat remains hyperintense and may confound interpretation. We aimed to develop and evaluate SPOT-DIXON, a dual bright- and black-blood LGE sequence with Dixon water-fat separation and artificial intelligence (AI)-guided analysis, enabling simultaneous and co-registered quantification of myocardial scar and intra- and extra-cardiac fat. Methods SPOT-DIXON alternates bright- and black-blood LGE preparations on consecutive heartbeats while acquiring dual-echo data for Dixon water-fat separation. Simulations, phantom experiments, and validations in a prospective cohort of 36 patients referred for cardiac MRI were performed. An AI model automatically segmented the left ventricular (LV) wall on bright-blood water images, with contours propagation to scar and fat images. Results Simulations and phantoms confirmed preserved bright- and black-blood contrast and accurate water-fat separation. In patients, AI-based LV wall segmentation showed excellent agreement with manual delineation (global DICE=84.4 ± 4.0%). SPOT-DIXON yielded higher scar volume, burden, and transmurality measurements than reference sequences, while LV wall volume was similar between sequences. Novel quantitative metrics, including intramyocardial fat burden expressed relative to scar mass, were feasible only with SPOT-DIXON. Acquisition times were comparable across sequences (5min55s ± 2min17 s), while AI-based analysis substantially reduced post-processing time to 2.1 ± 0.7 seconds. Conclusion AI-guided SPOT-DIXON enables simultaneous, co-registered assessment of myocardial scar and fat within a single LGE acquisition. This approach overcomes key limitations of current workflows and introduces novel quantitative markers of fibrofatty remodelling with potential clinical relevance in cardiomyopathies.
Reference Key
openalex_W7225080739 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors
Journal European Heart Journal - Imaging Methods and Practice
Year 2026
DOI
10.1093/ehjimp/qyag188
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.