Convolution Neural Network for Objective Myocardial Viability Assessment Based on Regional Wall Thickness Quantification From Cine-MR Images

Abstract

In clinical routine, the assessment of myocardial viability is based on visual analysis of late gadolinium enhancement (LGE) sequences. This procedure remains subjective and insufficient, particularly in cases of improbable viability, where scar transmurality spans 25% to 75% of the myocardial segment total thickness. To address these challenges, this paper introduces a novel framework based on deep convolutional neural network (CNN) for objective and quantitative assessment of myocardial viability. The proposed method was validated on 73 patients with myocardial infarction and 10 healthy subjects. The initial stage involves the automatic quantification of regional myocardial wall thickness (MWT) in multiple radial directions to offer a detailed regional assessment compared to traditional techniques. This method is based on automatic segmentation of the left ventricle contours using U-Net. Afterwards, we proposed a novel protocol to automate the classification of myocardial segments into viable and nonviable classes. Additionally, we introduced MWT as a new key parameter for studying peri-infarct areas. Comparative study of our method to related works proves its superiority with an average mean absolute error (MAE) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.21~\pm ~1.00$ </tex-math></inline-formula>. Accurate quantification of MWT allowed the detection of myocardial segments desynchronization and the delimitation of infarction transmurality with an accuracy of 98.13%, a specificity of 99.09% and a sensitivity of 97.52% with (p_value <0.001). The obtained results proved that incorporating the proposed protocol in clinical practice may facilitate the differentiation between viable and non-viable segments, aiding in directing patient care and minimizing intra and inter-observer variability.