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Diastolic function assessment with four-dimensional flow cardiovascular magnetic resonance using automatic deep learning E/A ratio analysis
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-0354-7680
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). deCODE Genet Amgen Inc, Iceland.
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Univ Calif San Francisco, CA USA.
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Heart Center, Department of Clinical Physiology in Linköping.ORCID iD: 0000-0002-5716-5098
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2024 (English)In: Journal of Cardiovascular Magnetic Resonance, ISSN 1097-6647, E-ISSN 1532-429X, Vol. 26, no 1, article id 101042Article in journal (Refereed) Published
Abstract [en]

Background: Diastolic left ventricular (LV) dysfunction is a powerful contributor to the symptoms and prognosis of patients with heart failure. In patients with depressed LV systolic function, the E/A ratio, the ratio between the peak early (E) and the peak late (A) transmitral flow velocity, is the first step to defining the grade of diastolic dysfunction. Doppler echocardiography (echo) is the preferred imaging technique for diastolic function assessment, while cardiovascular magnetic resonance (CMR) is less established as a method. Previous four-dimensional (4D) Flow -based studies have looked at the E/A ratio proximal to the mitral valve, requiring manual interaction. In this study, we compare an automated, deep learning -based and two semi -automated approaches for 4D Flow CMR-based E/A ratio assessment to conventional, gold -standard echo -based methods. Methods: Ninety-seven subjects with chronic ischemic heart disease underwent a cardiac echo followed by CMR investigation. 4D Flow -based E/A ratio values were computed using three different approaches; two semi -automated, assessing the E/A ratio by measuring the inflow velocity (MVvel) and the inflow volume (MVflow) at the mitral valve plane, and one fully automated, creating a full LV segmentation using a deep learning -based method with which the E/A ratio could be assessed without constraint to the mitral plane (LVvel). Results: MVvel, MVflow, and LVvel E/A ratios were strongly associated with echocardiographically derived E/A ratio (R 2 = 0.60, 0.58, 0.72). LVvel peak E and A showed moderate association to Echo peak E and A, while MVvel values were weakly associated. MVvel and MVflow EA ratios were very strongly associated with LVvel (R 2 = 0.84, 0.86). MVvel peak E was moderately associated with LVvel, while peak A showed a strong association (R 2 = 0.26, 0.57). Conclusion: Peak E, peak A, and E/A ratio are integral to the assessment of diastolic dysfunction and may expand the utility of CMR studies in patients with cardiovascular disease. While underestimation of absolute peak E and A velocities was noted, the E/A ratio measured with all three 4D Flow methods was strongly associated with the gold standard Doppler echocardiography. The automatic, deep learning -based method performed best, with the most favorable runtime of similar to 40 seconds. As both semi -automatic methods associated very strongly to LVvel, they could be employed as an alternative for estimation of E/A ratio.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC , 2024. Vol. 26, no 1, article id 101042
Keywords [en]
4D Flow CMR; Diastolic function; EA ratio; Deep learning
National Category
Cardiology and Cardiovascular Disease
Identifiers
URN: urn:nbn:se:liu:diva-204386DOI: 10.1016/j.jocmr.2024.101042ISI: 001233629500001PubMedID: 38556134OAI: oai:DiVA.org:liu-204386DiVA, id: diva2:1868988
Note

Funding Agencies|Swedish Research Council [2022-03931]; Swedish Heart and Lung Foundation [20210441]; ALF Grants Region stergtland [R-987498]; Sweden's Innovation Agency Vinnova [2019-02261]; EU [223615]

Available from: 2024-06-12 Created: 2024-06-12 Last updated: 2025-02-10

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Viola, FedericaBustamante, MarianaBolger, Ann FEngvall, JanEbbers, Tino
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Division of Diagnostics and Specialist MedicineFaculty of Medicine and Health SciencesCenter for Medical Image Science and Visualization (CMIV)Department of Clinical Physiology in Linköping
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Journal of Cardiovascular Magnetic Resonance
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