Gerardo I. Sanchez-Ortiz

Registration and tracking to integrate X-ray and MR images in an XMR Facility

We describe a registration and tracking technique to integrate cardiac X-ray images and cardiac magnetic resonance (MR) images acquired from a combined X-ray and MR interventional suite (XMR). Optical tracking is used to determine the transformation matrices relating MR image coordinates and X-ray image coordinates. Calibration of X-ray projection geometry and tracking of the X-ray C-arm and table enable three-dimensional (3-D) reconstruction of vessel centerlines and catheters from bi-plane X-ray views. We can, therefore, combine single X-ray projection images with registered projection MR images from a volume acquisition, and we can also display 3-D reconstructions of catheters within a 3-D or multi-slice MR volume. Registration errors were assessed using phantom experiments. Errors in the combined projection images (two-dimensional target registration error - TRE) were found to be 2.4 to 4.2 mm, and the errors in the integrated volume representation (3-D TRE) were found to be 4.6 to 5.1 mm. These errors are clinically acceptable for alignment of images of the great vessels and the chambers of the heart. Results are shown for two patients. The first involves overlay of a catheter used for invasive pressure measurements on an MR volume that provides anatomical context. The second involves overlay of invasive electrode catheters (including a basket catheter) on a tagged MR volume in order to relate electrophysiology to myocardial motion in a patient with an arrhythmia. Visual assessment of these results suggests the errors were of a similar magnitude to those obtained in the phantom measurements.

Atlas-Based Segmentation and Tracking of 3D Cardiac MR Images Using Non-rigid Registration

We propose a novel method for fully automated segmentation and tracking of the myocardium and left and right ventricles (LV and RV) using 4D MRimages. The method uses non-rigid registration to elastically deform a cardiac atlas built automatically from 14 normal subjects. The registration yields robust performance and is particularly suitable for processing a sequence of 3D images in a cardiac cycle. Transformations are calculated to obtain the deformations between images in a sequence. The registration algorithm aligns the cardiac atlas to a subject specific atlas of the sequence generated with the transformations. The method relates images spatially and temporally and is suitable for measuring regional motion and deformation, as well as for labelling and tracking specific regions of the heart. In this work experiments for the registration, segmentation and tracking of a cardiac cycle are presented on nine MRI data sets. Validation against manual segmentations and computation of the correlation between manual and automatic tracking and segmentation on 141 3D volumes were calculated. Results show that the procedure can accurately track the left ventricle (r=0.99), myocardium (r=0.98) and right ventricle (r=0.96). Results for segmentation are also obtained for left ventricle (r=0.92), myocardium (r=0.82) and right ventricle (r=0.90).

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.

Simulation of cardiac pathologies using an electromechanical biventricular model and XMR interventional imaging

Simulating cardiac electromechanical activity is of great interest for a better understanding of pathologies and for therapy planning. Design and validation of such models is difficult due to the lack of clinical data. XMR systems are a new type of interventional facility in which patients can be rapidly transferred between X-ray and MR systems. Our goal is to design and validate an electromechanical model of the myocardium using XMR imaging. The proposed model is computationally fast and uses clinically observable parameters. We present the integration of anatomy, electrophysiology, and motion from patient data. Pathologies are introduced in the model and simulations are compared to measured data. Initial qualitative comparison on the two clinical cases presented is encouraging. Once fully validated, these models will make it possible to simulate different interventional strategies.

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.

Segmentation of 4D Cardiac MR Images Using a Probabilistic Atlas and the EM Algorithm

In this paper an automatic atlas-based segmentation algorithm for 4D cardiac MR images is proposed. The algorithm is based on the 4D extension of the expectation maximisation (EM) algorithm. The EM algorithm uses a 4D probabilistic cardiac atlas to estimate the initial model parameters and to integrate a-priori information into the classification process. The probabilistic cardiac atlas has been constructed from the manual segmentations of 3D cardiac image sequences of 14 subjects. It provides space and time-varying probability maps for the left and right ventricle, the myocardium, and background structures such as the liver, stomach, lungs and skin. In addition to the probabilistic cardiac atlas, the segmentation algorithm incorporates spatial and temporal contextual information by using 4D Markov Random Fields (MRF). Validation against manual segmentations and computation of the correlation between manual and automatic segmentation on 249 3D volumes were calculated. Results show that the procedure can successfully segment the left ventricle (LV) (r=0.95), myocardium (r=0.83) and right ventricle (RV) (r=0.91).

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.

Knowledge-based tensor anisotropic diffusion of cardiac magnetic resonance images

We present a general formulation for a new knowledge-based approach to anisotropic diffusion of multi-valued and multi-dimensional images, with an illustrative application for the enhancement and segmentation of cardiac magnetic resonance (MR) images. In the proposed method all available information is incorporated through a new definition of the conductance function which differs from previous approaches in two aspects. First, we model the conductance as an explicit function of time and position, and not only of the differential structure of the image data. Inherent properties of the system (such as geometrical features or non-homogeneous data sampling) can therefore be taken into account by allowing the conductance function to vary depending on the location in the spatial and temporal coordinate space. Secondly, by defining the conductance as a second-rank tensor, the non-homogeneous diffusion equation gains a truly anisotropic character which is essential to emulate and handle certain aspects of complex data systems. The method presented is suitable for image enhancement and segmentation of single- or multi-valued images. We demonstrate the efficiency of the proposed framework by applying it to anatomical and velocity-encoded cine volumetric (4-D) MR images of the left ventricle. Spatial and temporal a priori knowledge about the shape and dynamics of the heart is incorporated into the diffusion process. We compare our results to those obtained with other diffusion schemes and exhibit the improvement in regions of the image with low contrast and low signal-to-noise ratio.

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.