Radiomics feature based discrimination between aortic flow profiles in HFpEF and healthy controls using 2D Flow MRI

Motivation: HFpEF (heart failure with preserved ejection fraction) patients show similar diagnostic parameter values to healthy subjects in the quantitative evaluation of cardiac magnetic resonance imaging (CMR). Improving the performance of CMR-based HF characterization is desirable. Goal(s): Evaluation of radiomics features from 2D flow MRI of the aorta for the classification into healthy, HFpEF, HFmrEF (mildly reduced) and HFrEF (reduced). Approach: Training and evaluation of a decision tree classifier using 70 CMR datasets with conventional diagnostic parameters and 2D flow radiomics features to find relevant flow descriptors. Results: The classifier achieved excellent results, considering the local flow homogeneity during diastole. Impact: We evaluate adding 2D flow MRI radiomics to standard EF measurements for the classification of heart failure subtypes. This would enable the diagnosis of the subtypes with one imaging modality without the need for stress testing.