Automatic assessment of regional and global wall motion abnormalities in echocardiography images by nonlinear dimensionality reduction. Issue 5 (25th April 2013)

• Record Type: Journal Article
• Authors: Shalbaf, Ahmad; Behnam, Hamid; Alizade‐Sani, Zahra; Shojaifard, Maryam
• Is Part Of: Medical physics. Volume 40:Issue 5(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-04-25
• Subjects: Ultrasonography -- Registration -- Reconstruction -- Image quality -- Mechanical and electrical properties of tissues and organs -- Probability theory, stochastic processes, and statistics; biomechanics -- echocardiography -- feature extraction -- image classification -- image reconstruction -- image registration -- medical image processing -- motion estimation -- nonlinear estimation -- regression analysis; echocardiography images -- LV motion -- registration -- NLDR; Echo‐tomography -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Analysis of motion; Medical imaging -- Manifolds -- Cardiac dynamics -- Image registration -- Resistive wall mode -- Medical image segmentation -- Heart -- Cardiac haemodynamics -- Data sets -- Medical magnetic resonance imaging; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4799840
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : Identification and assessment of left ventricular (LV) global and regional wall motion (RWM) abnormalities are essential for clinical evaluation of various cardiovascular diseases. Currently, this evaluation is performed visually which is highly dependent on the training and experience of echocardiographers and thus is prone to considerable interobserver and intraobserver variability. This paper presents a new automatic method, based on nonlinear dimensionality reduction (NLDR) for global wall motion evaluation and also detection and classification of RWM abnormalities of LV wall in a three‐point scale as follows: (1) normokinesia, (2) hypokinesia, and (3) akinesia. Methods: : Isometric feature mapping (Isomap) is one of the most popular NLDR algorithms. In this paper, a modified version of Isomap algorithm, where image to image distance metric is computed using nonrigid registration, is applied on two‐dimensional (2D) echocardiography images of one cycle of heart. By this approach, nonlinear information in these images is embedded in a 2D manifold and each image is characterized by a point on the constructed manifold. This new representation visualizes the relationship between these images based on LV volume changes. Then, a new global and regional quantitative index from the resultant manifold is proposed for global wall motion estimation and also classification of RWM of LV wall in a three‐point scale. Obtained results by our method are quantitatively evaluated to those obtained visually by two experienced echocardiographers as the reference (gold standard) on 10 healthy volunteers and 14 patients. Results: : Linear regression analysis between the proposed global quantitative index and the global wall motion score index and also with LV ejection fraction obtained by reference experienced echocardiographers resulted in the correlation coefficients of 0.85 and 0.90, respectively. Comparison between the proposed automatic RWM scoring and the reference visual scoring resulted in an absolute agreement of 82% and a relative agreement of 97%. Conclusions: : The proposed diagnostic method can be used as a useful tool as well as a reference visual assessment by experienced echocardiographers for global wall motion estimation and also classification of RWM abnormalities of LV wall in a three‐point scale in clinical evaluations.