Accurate delineation of the volumetric motion of Left-Ventricle (LV) of the heart from tagged Magnetic Resonance Imaging (MRI) is an important area of research. We have built a system that takes extracted tag line features from short-axis (SA) and long-axis (LA) image sequences as input, and fits a 4D time-varying B-spline model to the data by simultaneously fitting the model knot solids to MRI frames via matching three sequences of solid knot planes to the LV tag planes for 4D tracking. An important advantage of the model is that 3D material point localization and displacement reconstruction is achieved in a single step. The generated 3D displacement fields are validated with a cardiac motion simulator, and 3D motion fields capturing in-vivo deformations in a porcine model with anterior myocardial infarction are illustrated. Keywords: Cardiac Motion, Tagged MRI, B-spline Deformable Model, Myocardial Infarction. 1 Introduction Noninvasive imaging techniques for assessing the dynamic ...

A major issue in cardiac imaging is the assessment of cardiac function and particularly the identification of ischemic or infarcted tissues. We present in this article a method to reconstruct the displacement field of the left ventricular (LV) motion using 4D planispheric transformations of time and space combined in a first step with B-spline tensor products. Because of the 4D modeling, a) it is possible to include any tag plane direction as input data. b) The use of planispheric coordinates makes the numerical evaluation more stable as compared to prolate spheroidal coordinates, the equivalent focal point being much further from the apical area of the heart. This therefore avoids mathematical instability when the material points of the myocardium are too close to the apical focus. c) In the temporal modeling, a simple adaptation is possible to changing temporal dynamics, suchasintroduced by ectopic pacing or rapid filling after systole. d) Finally, the strain analysis and displacemen...

Tagged magnetic resonance imaging (MRI) has shown great promise in noninvasive analysis of heart motion. To replace implanted markers as a gold standard, however, tagged MRI must be able to track a sparse set of material points, so-called material markers, with high accuracy. This paper presents a new method for generating accurate motion estimates over a sparse set of material points using standard, parallel-tagged MR images. The tracked points are located at intersections of three tag surfaces, each of which is estimated using a thinplate spline. The intersections are determined by an iterative alternating projections algorithm for which a proof of convergence is provided. The resulting data sets are compatible with applications developed to exploit implanted marker data. One set of these material markers from a normal human volunteer is examined in detail using several methods to visualize the markers. Numerical results that include additional studies are also discussed. Finally, an...

Magnetic resonance (MR) tagging has been shown to be a useful technique for non-invasively measuring the deformation of an in vivo heart. An important step in analyzing tagged images is the identification of tag lines in each image of a cine sequence. Most existing tag identification algorithms require user-defined myocardial contours. Contour identification, however, is time consuming and requires a considerable amount of user intervention. In this paper, a new method for identifying tag lines, which we call the ML/MAP method, is presented that does not require user-defined myocardial contours. The ML/MAP method is composed of three stages. First a set of candidate tag line centers are estimated across the entire region-ofinterest (ROI) with a snake algorithm based on a maximumlikelihood (ML) estimate of the tag center. Next a maximum a posteriori (MAP) hypothesis test is used to detect the candidate tag centers that are actually part of a tag line. Finally a pruning algorithm is used...

Magnetic Resonance (MR) tagging has been shown to be a useful method for non-invasively measuring the deformation of the left ventricle (LV), during the cardiac cycle. By reconstructing a displacement field based on the movement of the tag lines, one can compute myocardial contraction measures such as strain. Existing methods depend on user-defined LV contours, which require human intervention and are therefore the biggest bottleneck in the reconstruction process. In this paper we present a method for reconstructing 2-D LV deformation without user-defined contours. We use a compound Gauss-Markov random field to model the 2-D vector displacement field, which is parameterized by two closed and smooth contours. By iteratively optimizing the contours, the displacement field, and the parameters, we obtain an estimate of the displacement field and the contours. Experimental results on in vivo human data are presented that demonstrate the accuracy of our algorithm. 1. Introduction Magnetic r...

The assessment of regional heart wall motion (local strain) can localize ischemic myocardial disease, evaluate myocardial viability and identify impaired cardiac function due to hypertrophic or dilated cardiomyopathies. The objectives of this research were to develop and validate a technique known as Hyperelastic Warping for the measurement of local strains in the left ventricle from clinical cine-MRI image datasets. The technique uses differences in image intensities between template (reference) and target (loaded) image datasets to generate a body force that deforms a finite element (FE) representation of the template so that it registers with the target image. To validate the technique, MRI image datasets representing two deformation states of a left ventricle were created such that the deformation map between the states represented in the images was known. A beginning diastolic cine-MRI image dataset from a normal human subject was defined as the template. A second image dataset (target) was created by mapping the template image using the deformation results obtained from a forward FE model of diastolic filling. Fiber stretch and strain predictions from Hyperelastic Warping showed good agreement with those of the forward solution (R 2 = 0.67 stretch, R 2 = 0.76 circumferential strain, R 2 = 0.75