ArticleEuropean heart journal. Imaging methods and practice2026
Automated whole-heart volumetrics and haemodynamics from 4D flow CMR magnitude images: development and validation of a deep learning model.
Article in European heart journal. Imaging methods and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Aims: 4D flow cardiovascular magnetic resonance (CMR) offers a comprehensive haemodynamic assessment but is often limited by long acquisition times and complex post-processing. The magnitude images derived from 4D flow sequences contain time-resolved 3D anatomical information. We aimed to validate the anatomical accuracy of these images against standard cine imaging and develop an artificial intelligence (AI) model for automated segmentation to facilitate analysis. Methods and results: Forty patients prospectively identified from the PREFER-CMR registry underwent CMR, including standard cine stacks and 4D flow. The study consisted of two stages. In Stage 1, manual segmentation of the cardiac chambers and great vessels was performed on 4D flow magnitude images. These were validated against standard cine volumetrics (LV/RV) and normative reference values (LA/RA). In Stage 2, a fully automated deep learning algorithm was trained and validated. Advanced haemodynamic metrics were derived using both manual and AI segmentations to assess agreement. The study cohort ( Conclusion: 4D flow magnitude imaging provides accurate volumetrics. Deep learning automation of this process is feasible, allowing for rapid, comprehensive assessment of cardiac structure, function, and advanced energetics.
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