Quantification of left ventricular function in MRI: a review of current approaches

• Main Authors: wafa, Baccouch, Sameh, Oueslati, Salam, Labidi, Basel, Solaiman
• Format: Conference Proceeding
• Language: English
• Subjects: cardiac MRI; deep learning; parametric imaging; quantification; segmentation
• ISSN: 2687-878X
• DOI: 10.1109/ATSIP49331.2020.9231709

Detecting and quantifying abnormalities in the movement of the heart walls such as hypokinesia, akinesia and dyskinesia and measuring their severity is a critical step in the assessment and treatment of ischemic and non-ischemic heart disease. These so-called contraction abnormalities are generally manifested by a decrease in the amplitude of the cardiac contraction reflecting hypokinesia and a complete absence of wall movement indicating akinesia. In case of dyskinesia, the wall is characterized by an abnormal movement, most often ventricular. In the non-pathological case, when the ventricle contracts in systole, it thickens and tends to approach the center of the cavity while in case of dyskinesia it tends to move away. In medical imaging, several methods for regional assessment of cardiac contractile function have been developed. The aim of this article is to review the most relevant approaches available in magnetic resonance imaging (MRI) such as parametric imaging, cardiac contour segmentation and deep learning. At the end of this study, we compared the previously mentioned approaches after explaining their principles, their advantages and disadvantages. The comparison showed that deep learning represents the most precise method in terms of segmentation and quantification of the contraction anomalies.