Fully automatic left ventricular myocardial strain estimation in 2D short-axis tagged magnetic resonance imaging. (7th August 2017)

• Record Type: Journal Article
• Authors: Morais, Pedro; Queirós, Sandro; Heyde, Brecht; Engvall, Jan; 'hooge, Jan D; Vilaça, João L
• Is Part Of: Physics in medicine & biology. Volume 62:Number 17(2017:Sep.)
• Page Start: 6899
• Page End: 6919
• Publication Date: 2017-08-07
• Subjects: tagged magnetic resonance imaging -- fully automatic segmentation -- non-rigid image registration -- strain estimation; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aa7dc2
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Cardiovascular diseases are among the leading causes of death and frequently result in local myocardial dysfunction. Among the numerous imaging modalities available to detect these dysfunctional regions, cardiac deformation imaging through tagged magnetic resonance imaging (t-MRI) has been an attractive approach. Nevertheless, fully automatic analysis of these data sets is still challenging. In this work, we present a fully automatic framework to estimate left ventricular myocardial deformation from t-MRI. This strategy performs automatic myocardial segmentation based on B-spline explicit active surfaces, which are initialized using an annular model. A non-rigid image-registration technique is then used to assess myocardial deformation. Three experiments were set up to validate the proposed framework using a clinical database of 75 patients. First, automatic segmentation accuracy was evaluated by comparing against manual delineations at one specific cardiac phase. The proposed solution showed an average perpendicular distance error of 2.35 ± 1.21 mm and 2.27 ± 1.02 mm for the endo- and epicardium, respectively. Second, starting from either manual or automatic segmentation, myocardial tracking was performed and the resulting strain curves were compared. It is shown that the automatic segmentation adds negligible differences during the strain-estimation stage, corroborating its accuracy. Finally, segmental strain was compared with scar tissue extent determined by delay-enhanced MRI. The results proved that both strain components were able to distinguish between normal and infarct regions. Overall, the proposed framework was shown to be accurate, robust, and attractive for clinical practice, as it overcomes several limitations of a manual analysis.