Raghavendra Chandrashekara

Analysis of 3-D myocardial motion in tagged MR images using nonrigid image registration

Tagged magnetic resonance imaging (MRI) is unique in its ability to noninvasively image the motion and deformation of the heart in vivo, but one of the fundamental reasons limiting its use in the clinical environment is the absence of automated tools to derive clinically useful information from tagged MR images. In this paper, we present a novel and fully automated technique based on nonrigid image registration using multilevel free-form deformations (MFFDs) for the analysis of myocardial motion using tagged MRI. The novel aspect of our technique is its integrated nature for tag localization and deformation field reconstruction using image registration and voxel based similarity measures. To extract the motion field within the myocardium during systole we register a sequence of images taken during systole to a set of reference images taken at end-diastole, maximizing the normalized mutual information between the images. We use both short-axis and long-axis images of the heart to estimate the full four-dimensional motion field within the myocardium. We also present validation results from data acquired from twelve volunteers.

Construction of a Statistical Model for Cardiac Motion Analysis Using Nonrigid Image Registration

In this paper we present a new technique for tracking the movement of the myocardium using a statistical model derived from the motion fields in the hearts of several healthy volunteers. To build the statistical model we tracked the motion of the myocardium in 17 volunteers using a nonrigid registration technique based on free-form deformations and mapped the motion fields obtained into a common reference coordinate system. A principal component analysis (PCA) was then performed on the motion fields to extract the major modes of variation in the fields between the successive time frames. The modes of variation obtained were then used to parametrize the free-form deformations and build our statistical model. The results of using our model to track the motion of the heart in normal volunteers are also presented.

Spatial transformation of motion and deformation fields using nonrigid registration

In this paper, we present a technique that can be used to transform the motion or deformation fields defined in the coordinate system of one subject into the coordinate system of another subject. Such a transformation accounts for the differences in the coordinate systems of the two subjects due to misalignment and size/shape variation, enabling the motion or deformation of each of the subjects to be directly quantitatively and qualitatively compared. The field transformation is performed by using a nonrigid registration algorithm to determine the intersubject coordinate system mapping from the first subject to the second subject. This fixes the relationship between the coordinate systems of the two subjects, and allows us to recover the deformation/motion vectors of the second subject for each corresponding point in the first subject. Since these vectors are still aligned with the coordinate system of the second subject, the inverse of the intersubject coordinate mapping is required to transform these vectors into the coordinate system of the first subject, and we approximate this inverse using a numerical line integral method. The accuracy of our numerical inversion technique is demonstrated using a synthetic example, after which we present applications of our method to sequences of cardiac and brain images.

Analysis of myocardial motion in tagged MR images using nonrigid image registration

Tagged magnetic resonance imaging (MRI) is unique in its ability to non-invasively image the motion and deformation of the heart in-vivo, but one of the fundamental reasons limiting its use in the clinical environment is the absence of automated tools to derive clinically useful information from tagged MR images. In this paper we present a novel and fully automated technique based on nonrigid image registration using multi-level free-form deformations (MFFDs) for the analysis of myocardial motion using tagged MRI. The novel aspect of our technique is its integrated nature for tag localization and deformation field reconstruction. To extract the motion field within the myocardium during systole we register a sequence of images taken during systole to a set of reference images taken at end-diastole, maximizing the mutual information between images. We use both short-axis and long-axis images of the heart to estimate the full four-dimensional motion field within the myocardium. We have validated our method using a cardiac motion simulator and we also present quantitative comparisons of cardiac motion from nine volunteers.

Comparison of Cardiac Motion Across Subjects Using Non-rigid Registration

We present a novel technique that enables a direct quantitative comparison of cardiac motion derived from 4D MR image sequences to be made either within or across patients. This is achieved by registering the images that describe the anatomy of both subjects and then using the computed transformation to map the motion fields of each subject into the same coordinate system. The motion fields are calculated by registering each of the frames in a sequence of tagged short-axis MRI images to the end-diastolic frame using a non-rigid registration technique based on multi-level free-form deformations. The end-diastolic untagged short-axis images acquired shortly after the tagged images were obtained are registered using non-rigid registration to determine an inter-subject mapping, which is used to transform the motion fields of one of the subjects into the coordinate system of the other, which is thus our reference coordinate system. The results show the transformed myocardial motion fields of a series of volunteers, and clearly demonstrate the potential of the proposed technique.

Cardiac motion tracking in tagged MR images using a 4D B-spline motion model and nonrigid image registration

We present a new method for tracking the motion of the heart in tagged MR images using a nonrigid image registration technique based on a 4D B-spline transformation model and the maximization of mutual information. We track the motion of the heart in a sequence of tagged MR images by registering the images taken during systole to a set of reference images taken at end-diastole. Registration is achieved by maximizing the mutual information between the sequence of images taken during systole with the reference images taken at end-diastole. The resulting deformation field is spatially and temporally smooth, and as such allows quantities such as strains and velocities to be calculated at any point in both space and time. We also present results from the motion tracking in a group of normal volunteers.

Nonrigid image registration with subdivision lattices: application to cardiac MR image analysis

In this paper we present a new methodology for cardiac motion tracking in tagged MRI using nonrigid image registration based on subdivision surfaces and subdivision lattices. We use two sets of registrations to do the motion tracking. First, a set of surface registrations is used to create and initially align the subdivision model of the left ventricle with short-axis and long-axis MR images. Second, a series of volumetric registrations are used to perform the motion tracking and to reconstruct the 4D cardiac motion field from the tagged MR images. The motion of a point in the myocardium over time is calculated by registering the images taken during systole to the set of reference images taken at end-diastole. Registration is achieved by optimizing the positions of the vertices in the base lattice so that the mutual information of the images being registered is maximized. The presented method is validated using a cardiac motion simulator and we also present strain measurements obtained from a group of normal volunteers.

Construction of a cardiac motion atlas from MR using non-rigid registration

In this paper we present a technique for constructing a cardiac motion atlas using the myocardial motion fields derived from 4D MR image sequences of a series of subjects. This is achieved by transforming the motion field of each subject into a the coordinate system of a reference subject, and then averaging the transformed fields to give a vector field representing the mean motion of the heart. The motion fields of each subject are calculated by registering each of the frames in the sequence of tagged short-axis and long-axis MRI images to the end-diastolic frame using a non-rigid registration technique based on multi-level free-form deformations. The end-diastolic untagged short-axis images of each subject, which are acquired shortly after the tagged images, are registered to the corresponding image of a designated reference subject using non-rigid registration to determine reference-subject mappings, which are then used to transform the corresponding motion fields into that of the reference subject. Finally, the mean transformed motion field is calculated to give the cardiac motion atlas.

Building a 4D atlas of the cardiac anatomy and motion using MR imaging

In this paper we describe the construction of 4D atlas of human heart using cardiac MR imaging. This probabilistic atlas captures the cardiac anatomy and function of a healthy heart. In order to build the atlas we have acquired tagged as well as untagged MR image sequences from 11 healthy volunteers. The untagged MR image sequences for each subject are segmented and then mapped into a common reference coordinate system using a novel spatio-temporal registration algorithm to produce a 4D probabilistic model of the cardiac anatomy. In addition, the tagged MR image sequences are used to derive motion fields between the end-diastolic and the end-systolic frames which describe myocardial contraction patterns in each subject. These motion fields are also mapped into our spatio-temporal reference coordinate system to produce a 4D statistical model of cardiac function.

Comparison of cardiac motion fields from tagged and untagged MR images using nonrigid registration

This paper presents a comparison of the motion fields computed from TrueFISP untagged and SPAMM tagged magnetic resonance (MR) images using a 4D nonrigid registration algorithm that we have developed for cardiac motion tracking [3]. Our results, which were obtained from a group of 7 normal volunteers, indicate that although there is a good correlation between the motion fields computed from the tagged and untagged MR images, some of the twisting motion is not captured in the motion fields derived from the TrueFISP MR images.