J. Duncan and G. Gerig (Eds.): MICCAI 2005, LNCS 3749, pp. 778 – 785, 2005. © Springer-Verlag Berlin Heidelberg 2005 A Boundary Element-Based Approach to Analysis of LV Deformation Ping Yan 1,3 , Ning Lin 1,3 , Albert J. Sinusas 2,4 , and James S. Duncan 1,2,3 Departments of 1 Electrical Engineering, 2 Diagnostic Radiology, 3 Biomedical Engineering and 4 Medicine, Yale University PO Box 208042, New Haven, CT 06520-8042, USA ping.yan@yale.edu Abstract. Quantification of left ventricular (LV) deformation from 3D image sequences (4D data) is important for the assessment of myocardial viability, which can have important clinical implications. To date, feature information from either Magnetic Resonance, computed tomographic or echocardiographic image data has been assembled with the help of different interpolative models to estimate LV deformation. These models typically are designed to be computationally efficient (e.g. regularizing strategies using B-splines) or more physically realistic (e.g. finite element approximations to biomechanical models), but rarely incorporate both notions. In this paper, we combine an approach to the extraction and matching of image-derived point features based on local shape properties with a boundary element model. This overall scheme is intended to be both computationally efficient and physically realistic. In order to illustrate this, we compute strains using our method on canine 4D MR image sequences and compare the results to those found from a B-spline-based method (termed extended free-form deformation (EFFD)) and a method based on finite elements (FEM). All results are compared to displacements found using implanted markers, taken to be a gold standard. 1 Introduction Quantitative analysis of left ventricular (LV) deformation is known to be a sensitive index of myocardial ischemia and injury. However, while there have been many methods proposed for the estimation of LV deformation [1, 2, 6, 7], most employ some form of modeling to interpolate dense displacement fields from sparse image derived features. These features include shape-based measures [1], MR tags [3], MR phase velocity [4] and echocardiographic features [5]. These models suffer from an inherent tradeoff between the computation time and the complexity of the model. A biomechanical model constructed using FEM, which incorporates the microstructure of the LV, is considered as the model closest to the physical reality of the LV but solving for the parameters embedded in these models is usually time-consuming [2,6]. Recently, simple deformation models such as B-spline-based EFFD [8] have been developed to estimate the LV deformation [7]. This method is computationally efficient but typically doesn’t reflect the true physical properties of LV. Hence these