Classification of Mitral Regurgitation from Cardiac Cine MRI using Clinically-Interpretable Morphological Features

TL;DR

This paper presents a new method for automatically classifying mitral regurgitation from Cine MRI by extracting 4D morphological features of the mitral annulus, achieving promising accuracy with interpretable models.

Contribution

It introduces a novel approach to extract and select morphological features from Cine MRI for MR classification, combining clinical interpretability with machine learning.

Findings

Achieved ~73% accuracy in MR classification

Identified 25 key morphological features using MRMR

Demonstrated effective use of LDA and RF models

Abstract

The assessment of mitral regurgitation (MR) using cardiac MRI, particularly Cine MRI, is a promising technique due to its wide availability. However, some of the temporal information available in clinical Cine MRI may not be fully utilised, as it requires detailed temporal analysis across different cardiac views. We propose a new approach to identify MR which automatically extracts 4-dimensional (3D + Time) morphological features from the reconstructed mitral annulus (MA) using Cine long-axis (LAX) views MRI. Our feature extraction involves locating the MA insertion points to derive the reconstructed MA geometry and displacements, resulting in a total of 187 candidate features. We identify the 25 most relevant mitral valve features using minimum-redundancy maximum-relevance (MRMR) feature selection technique. We then apply linear discriminant analysis (LDA) and random forest (RF) model to determine the presence of MR. Both LDA and RF demonstrate good performance, with accuracies of 0.72+/-0.05 and 0.73+/-0.09, respectively, in a 5-fold cross-validation analysis. This approach will be incorporated in an automatic tool to identify valvular diseases from Cine MRI by integrating both handcrafted and deep features. Our tool will facilitate the diagnosis of valvular disease from conventional cardiac MRI scans with no additional scanning or image analysis penalty. All code is made available on an open-source basis at: https://github.com/HenryOn2021/MA_Morphological_Features.