Catálogo de publicaciones - libros

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.

We extend our static multimodal nonrigid registration [1] to a spatio-temporal (2D+T) co-registration of a real-time 3D ultrasound and a cardiovascular MR sequence. The motivation for our research is to assist a clinician to automatically fuse the information from multiple imaging modalities for the early diagnosis and therapy of cardiac disease. The deformation field between both sequences is decoupled into spatial and temporal components. Temporal alignment is firstly performed to re-slice both sequences using a differential registration method. Spatial alignment is then carried out between the frames corresponding to the same temporal position. The spatial deformation is modeled by the polyaffine transformation whose anchor points (or control points) are automatically detected and refined by calculating a local mis-match measure based on phase mutual information. The spatial alignment is built in an adaptive multi-scale framework to maximize the phase-based similarity measure by optimizing the parameters of the polyaffine transformation. Results demonstrate that this novel method can yield an accurate registration to particular cardiac regions.

In this paper, we explore the use of fiber bundles extracted from diffusion MR images for a nonlinear registration algorithm. We employ a white matter atlas to automatically label major fiber bundles and to establish correspondence between subjects. We propose a polyaffine framework to calculate a smooth and invertible nonlinear warp field based on these correspondences, and derive an analytical solution for the reorientation of the tensor fields under the polyaffine transformation. We demonstrate our algorithm on a group of subjects and show that it performs comparable to a higher dimensional nonrigid registration algorithm.

Current clinical and research neuroimaging protocols acquire images using multiple modalities, for instance, T1, T2, diffusion tensor and cerebral blood flow magnetic resonance images (MRI). These multivariate datasets provide unique and often complementary anatomical and physiological information about the subject of interest. We present a method that uses fused multiple modality (scalar and tensor) datasets to perform intersubject spatial normalization. Our multivariate approach has the potential to eliminate inconsistencies that occur when normalization is performed on each modality separately. Furthermore, the multivariate approach uses a much richer anatomical and physiological image signature to infer image correspondences and perform multivariate statistical tests. In this initial study, we develop the theory for Multivariate Symmetric Normalization (MVSyN), establish its feasibility and discuss preliminary results on a multivariate statistical study of 22q deletion syndrome.

Accurate registration of cortical structures plays a fundamental role in statistical analysis of brain images across population. This paper presents a novel framework for the non-rigid intersubject brain surface registration, using conformal structure and spherical thin-plate splines. By resorting to the conformal structure, complete characteristics regarding the intrinsic cortical geometry can be retained as a mean curvature function and a conformal factor function defined on a canonical, spherical domain. In this transformed space, spherical thin-plate splines are firstly used to explicitly match a few prominent homologous landmarks, and in the meanwhile, interpolate a global deformation field. A post-optimization procedure is then employed to further refine the alignment of minor cortical features based on the geometric parameters preserved on the domain. Our experiments demonstrate that the proposed framework is highly competitive with others for brain surface registration and population-based statistical analysis. We have applied our method in the identification of cortical abnormalities in PET imaging of patients with neurological disorders and accurate results are obtained.

Hypothesis testing is an important way to detect the statistical difference between two populations. In this paper, we use the Fisher permutation and bootstrap tests to differentiate hippocampal shape between genders. These methods are preferred to traditional hypothesis tests which impose assumptions on the distribution of the samples. An efficient algorithm is adopted to rapidly perform the tests. We extend this algorithm to multivariate data by projecting the original data onto an “informative direction” to generate a scalar test statistic. This “informative direction” is found to preserve the original discriminative information. This direction is further used in this paper to isolate the discriminative shape difference between classes from the individual variability, achieving a visualization of shape discrepancy.

We propose a novel kidney segmentation approach based on the graph cuts technique. The proposed approach depends on both image appearance and shape information. Shape information is gathered from a set of training shapes. Then we estimate the shape variations using a new distance probabilistic model which approximates the marginal densities of the kidney and its background in the variability region using a Poisson distribution refined by positive and negative Gaussian components. To segment a kidney slice, we align it with the training slices so we can use the distance probabilistic model. Then its gray level is approximated with a LCG with sign-alternate components. The spatial interaction between the neighboring pixels is identified using a new analytical approach. Finally, we formulate a new energy function using both image appearance models and shape constraints. This function is globally minimized using / graph cuts to get the optimal segmentation. Experimental results show that the proposed technique gives promising results compared to others without shape constraints.

Non-linear attenuation of the Arterial Input Function (AIF) is a major problem in first-pass MR perfusion imaging due to the high concentration of the contrast agent in the blood pool. This paper presents a technique to reconstruct the true AIF using signal intensities in the myocardium and the attenuated AIF based on a Hierarchical Bayesian Model (HBM). With the proposed method, both the AIF and the response function are modeled as smoothed functions by using Bayesian penalty splines (P-Splines). The derived AIF is then used to estimate the impulse response of the myocardium based on deconvolution analysis. The proposed technique is validated both with simulated data using the MMID4 model and ten data sets for estimating myocardial perfusion reserve rates. The results demonstrate the ability of the proposed technique in accurately reconstructing the desired AIF for myocardial perfusion quantification. The method does not involve any MRI pulse sequence modification, and thus is expected to have wider clinical impact.

This paper presents a fast image synthesis procedure for elastic volumes under deformation. Given the node displacements of a mesh and the 3D image voxel data of an undeformed volume, the method maps the image plane pixels to be synthesized from the deformed configuration back to the nominal pre-deformed configuration, where the pixel intensities are obtained easily through interpolation in the regular-grid structure of the voxel volume. For smooth interpolation, this mapping requires the identification of the mesh element enclosing each image pixel. To accelerate this procedure, a fast method of marking the image pixels is employed by finding the intersection of the mesh and the image, and marking this intersection on the image pixels using . A deformable tissue phantom was constructed, it was modeled using the finite element method, and its 3D ultrasound volume was acquired in its undeformed state. Actual B-mode images of the phantom under deformation by the ultrasound probe were then compared with the corresponding synthesized images simulated for the same deformations. Results show that realistic images can be synthesized in real-time using the proposed technique.

In the last decade several methods for extracting the human cerebral cortex from magnetic resonance images have been proposed. Studies comparing these methods have been few. In this study we compare a recent cortical extraction method with FreeSurfer, which has been widespread in the scientific community during recent years. The comparison is performed using realistic phantoms generated from surfaces extracted from original brain scans. The geometrical accuracy of the reconstructed surfaces is compared to the surfaces extracted from the original scan. We found that our method is comparable with FreeSurfer in terms of accuracy, and in some cases it performs better. In terms of speed our method is more than 25 times faster.