Multi-task Deep Learning of Myocardial Blood Flow and Cardiovascular Risk Traits from PET Myocardial Perfusion Imaging

Advanced cardiac imaging with positron emission tomography (PET) is a powerful tool for the evaluation of known or suspected cardiovascular disease. Deep learning (DL) offers the possibility to abstract highly complex patterns to optimize classification and prediction tasks. We utilized DL models wi...

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Veröffentlicht in:	Journal of nuclear cardiology 2022-12, Vol.29 (6), p.3300-3310
Hauptverfasser:	Yeung, Ming Wai, Benjamins, Jan Walter, Knol, Remco J.J., van der Zant, Friso M., Asselbergs, Folkert W., van der Harst, Pim, Juarez-Orozco, Luis Eduardo
Format:	Artikel
Sprache:	eng
Schlagworte:	Cardiology Cardiovascular Diseases - diagnostic imaging Cardiovascular risk factors Coronary Artery Disease - diagnostic imaging Coronary Circulation Deep Learning Diabetes explainAI flow reserve Fractional Flow Reserve, Myocardial - physiology Humans Hypertension Hypertension - diagnostic imaging Imaging Medical image analysis Medicine Medicine & Public Health Metabolic disorders Myocardial perfusion Myocardial Perfusion Imaging - methods Nuclear Medicine Original Original Article PET imaging Positron-Emission Tomography Radiology Risk Factors Tomography, X-Ray Computed - methods
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Zusammenfassung:	Advanced cardiac imaging with positron emission tomography (PET) is a powerful tool for the evaluation of known or suspected cardiovascular disease. Deep learning (DL) offers the possibility to abstract highly complex patterns to optimize classification and prediction tasks. We utilized DL models with a multi-task learning approach to identify an impaired myocardial flow reserve (MFR
ISSN:	1071-3581 1532-6551
DOI:	10.1007/s12350-022-02920-x