Machine learning-based interpretation of non-contrast feature tracking strain analysis and T1/T2 mapping for assessing myocardial viability - GhaffariJolfayi, Amir; Salmanipour, Alireza; Heshmat-Ghahdarijani, Kiyan; MozafaryBazargany, MohammadHossein; Azimi, Amir; Pirouzi, Pirouz; Mohammadzadeh, Ali (Scientific reports, 2025)
Keywords: 692/4019/2776; 692/4019/592; Aged; Algorithms; Cardiovascular diseases; Cardiovascular magnetic resonance; Contrast Media; Coronary artery; Coronary vessels; Data acquisition; Feature tracking strain analysis; Female; Gadolinium; Heart diseases; Humanities and Social Sciences; Humans; Image Interpretation, Computer-Assisted - methods; Ischemia; Learning algorithms; Machine Learning; Magnetic Resonance Imaging - methods; Male; Mapping; Middle Aged; multidisciplinary; Myocardial infarction; Myocardial Infarction - diagnostic imaging; Myocardial viability; Myocardium - pathology; Neural networks; Population studies; Regression analysis; Renal function; Retrospective Studies; Science; Science (multidisciplinary); T1/T2 mapping; Viability; T1/T2 mapping; Myocardial viability; Cardiovascular magnetic resonance; Feature tracking strain analysis