Role of multimodality imaging in the diagnosis and management of cardiomyopathies.

Arch Cardiovasc Dis

Department of Cardiology, University Hospital Grenoble Alpes, 38700 La Tronche, France; INSERM, U-1039 (Radiopharmaceutiques Biocliniques), Grenoble Alpes University, 38700 La Tronche, France; French Alliance for Cardiovascular Trials, French Clinical Research Infrastructure Network, 31059 Toulouse, France. Electronic address:

Published: October 2019

Multimodality imaging plays an important role in the initial evaluation, diagnosis and management of patients suspected of having a cardiomyopathy. Beyond functional and anatomical information, multimodality imaging provides important variables that facilitate risk stratification and prognosis evaluation. Whatever the underlying suspected cardiomyopathy, echocardiography is the most common initial imaging test used to establish the presence of cardiomyopathy, by depicting structural and functional abnormalities. However, echocardiographic findings are non-specific, and therefore have a limited role in identifying the underlying aetiology. Cardiac magnetic resonance imaging allows characterization of myocardial tissue, which can be of great help in identifying the aetiology of the cardiomyopathy. When a specific aetiology is suspected, particularly inflammation, F-fluorodeoxyglucose positron emission tomography is recommended. The clinician should be capable of selecting the appropriate imaging techniques for each clinical scenario. Each technique has strengths and weaknesses, which should be known. In order to improve diagnostic performance, and as proposed by the European Association for Cardiovascular Imaging, cardiovascular imaging groups must be composed of experts from all modalities. The future of multimodality imaging in the diagnosis and management of cardiomyopathies will also involve evolution of its use in care, teaching and research. Training goals for future cardiac imaging experts must be defined; academic and industry partnerships should enable the connection to be made between imaging data and clinical data on the one hand and outcomes on the other hand, using big-data analysis and artificial intelligence.

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Source
http://dx.doi.org/10.1016/j.acvd.2019.07.004DOI Listing

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