Radiomics is a new emerging field that includes extraction of metrics and quantification of so-
called radiomic features from medical images. The growing importance of radiomics applied …

Background Cardiac magnetic resonance (MR) images are often collected with different
imaging parameters, which may impact the calculated values of myocardial radiomic …

Objective To evaluate the potential value of the machine learning (ML) models using
radiomic features of late gadolinium enhancement (LGE) and cine images on magnetic …

Objective To evaluate the feasibility of texture analysis on non-contrast-enhanced T1 maps
of cardiac magnetic resonance (CMR) imaging for the diagnosis of myocardial injury in …

Cardiovascular Magnetic Resonance Imaging (CMRI) is widely utilized for diagnosing
various heart diseases in routine clinical practice, providing information on heart …

Objective This work aimed to derive a machine learning (ML) model for the differentiation
between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) on non …

Objectives: To explore the incremental value of myocardial radiomics signature derived from
static coronary computed tomography angiography (CCTA) for identifying myocardial …

Aims: To evaluate the repeatability of cardiac magnetic resonance (CMR) radiomics features
on test-retest scanning using a multi-centre multi-vendor dataset with a varied case-mix …

Objectives: We sought to identify critical components of myocardial infarction (MI) including
area at risk (AAR), MI-core and salvageable zone (SZ) by using cardiac magnetic resonance …

The purpose of this study was to differentiate acute from chronic myocardial infarction using
machine learning techniques and texture features extracted from cardiac magnetic …