This work proposes a convolutional neural network (CNN) that utilizes different combinations of parametric images computed
from cine cardiac magnetic resonance (CMR) images, to classify each slice for possible myocardial scar tissue presence. The
CNN performance comparison in respect to expert interpretation of CMR with late gadolinium enhancement (LGE) images,
used as ground truth (GT), was conducted on 206 patients (158 scar, 48 control) from Centro Cardiologico Monzino (Milan,
Italy) at both slice- and patient-levels. Left ventricle dynamic features were extracted in non-enhanced cine images using
parametric images based on both Fourier and monogenic signal analyses. The CNN, fed with cine images and Fourier-based
parametric images, achieved an area under the ROC curve of 0.86 (accuracy 0.79, F1 0.81, sensitivity 0.9, specificity 0.65,
and negative (NPV) and positive (PPV) predictive values 0.83 and 0.77, respectively), for individual slice classification.
Remarkably, it exhibited 1.0 prediction accuracy (F1 0.98, sensitivity 1.0, specificity 0.9, NPV 1.0, and PPV 0.97) in patient
classification as a control or pathologic. The proposed approach represents a first step towards scar detection in contrast-free
CMR images. Patient-level results suggest its preliminary potential as a screening tool to guide decisions regarding LGECMR
prescription, particularly in cases where indication is uncertain.
This article is authored also by Synbrain data scientists and collaborators. READ THE FULL ARTICLE