Discriminative dictionary learning for local LV wall motion classification in cardiac MRI

摘要

The characterization of cardiac function is of high clinical interest for early diagnosis and better patient follow-up in cardiovascular diseases. A large number of cardiac image analysis methods and more precisely in cine-Magnetic Resonance Imaging (MRI) have been proposed to quantify both shape and motion parameters. However, the first major problem to address lies in the cardiac image segmentation that is most often needed to extract the myocardium before any other process such as motion tracking, or registration. Moreover, intelligent systems based on classification and learning techniques have emerged over the last years in medical imaging. In this paper, a new method is proposed to help medical experts in classifying the Left Ventricle (LV) wall motion without the need of image segmentation and through the learning of motion features by using dictionary learning techniques. Specifically the novelty of this approach lies in the extraction of new spatio-temporal descriptors and in the use of discriminative dictionary learning (DL) techniques to classify normal/abnormal LV function in cardiac MRI. Local radial spatio-temporal profiles are first extracted from bidimensional (2D) short axis (SAX) MRI images, for each anatomical segment of the LV cavity. These profiles inherently contain discriminatory information that can help for cardiac motion characterization. An advantage of this approach is that these profiles are constructed from a very limited user interaction that corresponds to a number of five points in only one frame of the sequence, (without the need of LV boundaries segmentation) and by exploiting all the phases of the cardiac cycle. Two specific discriminative DL algorithms have been selected for the LV wall classification based on these profiles: Label Consistent K-SVD (LC-KSVD) and Fisher Discriminative (FD-DL). For the application of the proposed methods, cine-MR SAX images have been collected from a control group of 9 healthy subjects and from 9 patients with cardiac dyssynchrony. Radial strain curves in 2D Speckle Tracking Echocardiography (2D-STE) have been analysed for the patient group and have been used as reference truth. They allowed to label each profile as normal or abnormal. The best performance has been achieved in the Wavelet domain by the LC-KSVD algorithm with an accuracy of 84.07% in the classification of radial spatio-temporal profiles and using a leave-one-out patient cross validation. The approach has been compared with recent methods of the literature and offers a good compromise between performance, user interaction, time computing and complexity. This new method of LV classification, with minimal user interaction and based on discriminative DL has not been previously reported. It could help to improve the performance of pre-screening systems for cardiac assessment, which can affect positively the quality of the early diagnosis for heart failure patients. (C) 2019 Published by Elsevier Ltd.