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

Autor:	Ming Wai Yeung, Jan Walter Benjamins, Remco J. J. Knol, Friso M. van der Zant, Folkert W. Asselbergs, Pim van der Harst, Luis Eduardo Juarez-Orozco
Přispěvatelé:	Cardiovascular Centre (CVC), Nuclear Medicine, ACS - Heart failure & arrhythmias
Rok vydání:	2022
Předmět:	Myocardial perfusion flow reserve Medical image analysis Nuclear medicine PET imaging Deep learning Radiology Nuclear Medicine and imaging explainAI Cardiology and Cardiovascular Medicine Cardiovascular risk factors
Zdroj:	Journal of Nuclear Cardiology, 29. SPRINGER Journal of nuclear cardiology, 29(6), 3300-3310. Springer New York
ISSN:	1532-6551 1071-3581
Popis:	Background 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. Methods and Results We utilized DL models with a multi-task learning approach to identify an impaired myocardial flow reserve (MFR Conclusion Multi-task DL for the evaluation of quantitative PET myocardial perfusion polar maps is able to identify an impaired MFR as well as cardiovascular risk traits such as sex, smoking and possibly diabetes at the individual-patient level.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::037f5ced2507e67713ceb48e2db14cc5 https://doi.org/10.1007/s12350-022-02920-x Zobrazit plný text záznamu Full text from SpringerLink