One of the most exciting recent advances in brain mapping is the. A distributedmemory solver for constrained large deformation diffeomorphic image registration. Scientific software environments scientific computing and. In this paper, we develop a novel nonlinear registration framework based on the diffeomorphic demons, where a reciprocal regularizer is introduced to assume that the deformation between two images is an exact diffeomorphism. Software developed with support from national institutes of health ncrr grant p41.
These diffeomorphisms are generated using the large deformation diffeomorphic metric mapping framework, i. I got x speed gain against a single cpu performance. Lddmm parameterizes a diffeo morphism through a time varying velocity field that is integrated through an ode. Pdf computing large deformation metric mappings via. The purpose of our study was to determine the usefulness of a ts technique based on an advanced nonrigid image registration algorithm, termed large deformation diffeomorphic metric mapping lddmm, in the detection of bone metastases on serial followup ct images. Large deformation diffeomorphic metric mappings theory, numerics. Increasing the accuracy of brain functional maps through large deformation diffeomorphic metric mapping by behrang nosrat makouei b. The large deformation diffeomorphic metric mapping lddmm tool is an application which aims to assign. Over the past several years we have been using large deformation diffeomorphic metric mapping lddmm joshi and miller 2000. We present a matching criterion for curves and integrate it into the large deformation diffeomorphic metric mapping lddmm scheme for computing an optimal transformation between two curves embedded in euclidean space. Large deformation diffeomorphism and momentum based. Geodesic registration on anatomical manifolds what is gram.
Association for computing machinery university of pennsylvania university of houston 0 share. Other packages from the cis include a rigid landmark matching rlm tool, caworks, brainworks, and mindview. We incorporate the riemannian metric of odfs for quantifying the similarity of two hardi images into a variational problem defined under the large deformation diffeomorphic metric mapping lddmm framework. As one of the applications of the atlas, automated brain segmentation was performed and the accuracy was measured using large deformation diffeomorphic metric mapping lddmm. This method not only provides a diffeomorphic correspondence. Changed large deformation metric mapping to large deformation diffeomorphic metric mapping lddmm on the web pages. This paper examine the eulerlagrange equations for the solution of the large deformation diffeomorphic metric mapping problem studied in dupuis et al. Abstractthis paper examine the eulerlagrange equations for the solution of the large deformation diffeomorphic metric mapping problem studied in dupuis et al. Beg proposed the large deformation diffeomorphic metric mapping lddmm algorithm miller et al. The atlaswerks software package provides efficient cpu and gpu implementations of nonlinear deformation algorithms based on the large deformation diffeomorphic metric mapping ldmm framework for single machines and clusters, command line frontends for atlas and deformation generation, and a number of utility programs for handling 3d medical. The multistage registration method may find broad application for mapping. The following diagram shows the effects of alpha values of 0.
Diffeomorphic metric mapping of high angular resolution diffusion imaging based on riemannian structure of orientation distribution functions. For instance, it could be used to quantify differences between male and female gorilla skull shapes, normal and pathological bone shapes, leaf outlines with and without herbivory by insects, etc. The large deformation diffeomorphic metric mapping lddmm software service aims to assign metric distances on the space of anatomical images in computational anatomy thereby allowing for the direct comparison and quantization of morphometric changes in shapes. Computational analysis of lddmm for brain mapping frontiers. This thesis first aims to validate the large deformation diffeomorphic metric mapping lddmm method used in the automated segmentation of three major subfields of the hippocampus known as cornu ammonis ca, dentate gyrus dg and subiculum sub on the 7t t1 mr images with the isotropic resolution of 0. But after some conversion it can be visualized using mayavi, paraview or slicer. In this study, we tested a nonlinear warping algorithm based on largedeformation, diffeomorphic metric mapping lddmm, to match images. Large deformation diffeomorphic metric curve mapping. These examples are useful tests when lddmm is run on new environments or platforms. Olmos, registration of anatomical images using paths of diffeomorphisms parameterized with. Large deformation diffeomorphic metric calculations were performed using the diffeomap software li et al. Robust diffeomorphic mapping via geodesically controlled. Statistical shape analysis is an analysis of the geometrical properties of some given set of shapes by statistical methods.
Curves are first represented as vectorvalued measures, which incorporate both location and the first order geometric structure of the curves. Correction of b0 susceptibility induced distortion in. Large deformation diffeomorphic metric mapping lddmm 1 has been recognized as a powerful approach for whole brain mapping. Gram is a framework for groupwise registration of medical images described in the paper by hamm, ye, verma, and davatzikos, media 2010. Lddmm is an advanced, nonlinear transformation method that enforces preservation of topology even with severe distortion. As part of these efforts the center for imaging science at johns hopkins university developed techniques to not only.
The lddmm validation section provides input data, processing and visualization examples for lddmm to ensure correctness of the resultant data. Images from step 4 were further registered by using the large deformation diffeomorphic metric mapping lddmm algorithm 24,25. Large deformation diffeomorphic metric mapping wikipedia. Fast templatebased shape analysis using diffeomorphic. This paper presents recent advances in the use of diffeomorphic active shapes which incorporate the conservation laws of large deformation diffeomorphic metric mapping. Large deformation diffeomorphic metric mapping registration of. The lddmm algorithm is one of the nonrigid image registration algorithms that originated from pattern theory 26, which was specifically designed for.
Important aspects of shape analysis are to obtain a measure of distance. Correction of b0 susceptibility induced distortion in diffusionweighted images using largedeformation diffeomorphic metric mapping. In this work, we propose a novel preconditioned optimization method in the paradigm of large deformation diffeomorphic metric mapping lddmm. It relies on a specific instance of the large deformation diffeomorphic metric mapping lddmm framework, based on control points. Computing large deformation metric mappings via geodesic. Large deformation diffeomorphic metric mapping lddmm is a specific suite of algorithms used for diffeomorphic mapping and manipulating dense imagery based on diffeomorphic metric mapping within the academic discipline of computational anatomy, to be distinguished from its precursor based on diffeomorphic mapping. Atlaswerks scientific computing and imaging institute. Figure 2 from computing large deformation metric mappings. Nonlinear image registration with bidirectional metric and. In large deformation diffeomorphic metric mapping lddmm, the diffeomorphic matching of images are modeled as evolution in time, or a flow, of an associated smooth velocity vector field v controlling the evolution.
Dec 01, 2008 we present a matching criterion for curves and integrate it into the large deformation diffeomorphic metric mapping lddmm scheme for computing an optimal transformation between two curves embedded in euclidean space. Its deformation is diffeomorphic and is constructed through a smooth vector field in a reproducing kernel hilbert space with diffeomorphic metric. In this paper we propose a fast approach for templatebased analysis of anatomical variability. Instead of applying the marching cubes algorithm directly, to generate a corresponding triangulated mesh from the segmentation of malf with the desired properties, we rely.
The output currently cannot be directly visualized as it only comprises of the velocity maps and the diffiomorphisms. Large deformation diffeomorphic metric mapping lddmm suite. After simulationbased validation of the algorithm, dti data from normal subjects. The following diagram is a basic overview of the software s dataflow.
Nonlinear registration is an important technique to align two different images and widely applied in medical image analysis. Multicontrast large deformation diffeomorphic metric. A large deformation diffeomorphic approach to registration of. Instead of applying the marching cubes algorithm directly, to generate a corresponding triangulated mesh from the segmentation of malf with the desired properties, we rely on deformation based shape generation in the setting of large deformation diffeomorphic metric mapping lddmm for surfaces vaillant and glaunes, 2005. In largedeformation diffeomorphic metric mapping lddmm, the diffeomorphic matching of images are modeled as evolution in time, or a flow, of an associated smooth velocity vector field v controlling the evolution. The large deformation diffeomorphic metric mapping framework is widely used for shape analysis of anatomical structures, but computing a template with such framework is computationally expensive. Currently we have the data flow in the lddmm software along with some sample. The registration uses diffeomorphisms that transform the template through a group action. Full text of the insight toolkit image registration.
Gaussnewton inspired preconditioned optimization in large. Multicontrast large deformation diffeomorphic metric mapping. After simulationbased validation of the algorithm, dti data from normal subjects were used to measure the registration accuracy. Nishikant deshmukh projects academic work experience academic projects. Hernandez, gaussnewton inspired preconditioned optimization in large deformation diffeomorphic metric mapping, physics in medicine and biology, 59 2014, pp. Detection of timevarying structures by large deformation. The equations of evolution satisfying the conservation law are geodesics under the diffeomorphism metric and therefore termed geodesically controlled diffeomorphic active shapes gdas. Pdf large deformation diffeomorphic metric mappings theory. Large deformation diffeomorphic metric mapping of fiber. Deformetrica is an opensource software for the statistical analysis of images and meshes. Large deformation diffeomorphic metric mapping lddmm is a widely used deformable registration algorithm for computing smooth invertible maps between various types of anatomical shapes such as. Figure 2 from computing large deformation metric mappings via.
The large deformation diffeomorphic metric mapping lddmm tool is an. I parallelized the lddmm large deformation diffeomorphic metric mapping volume software at cis on nvidia gpu. Used the threshold filter to clean up the velocity vector field. Large deformation diffeomorphic metric mapping lddmm aims to assign metric distances on the space of. Nevertheless, existing diffeomorphic metric is defined largely based on. Aug 15, 2009 in this paper, we evaluate the accuracy of normalization of dti data using a highly elastic transformation algorithm, called large deformation diffeomorphic metric mapping.
Large deformation diffeomorphic metric mapping registration. Notes implies the number of timesteps lddmm is ran over. Large deformation diffeomorphic metric mapping registration of reconstructed 3d histological section images and in vivo mr images. Semiautomated basal ganglia segmentation using large. In this paper, we examine the performance and complexity of such segmentation in the framework of the large deformation diffeomorphic metric mapping lddmm registration method with reference to atlases and parameters. Atlasbased whole brain white matter analysis using large. The lddmm algorithm is one of the nonrigid image registration algorithms that originated from pattern theory 26, which was specifically designed for a large amount of deformation. Large deformation diffeomorphic metric mapping registration of reconstructed 3d histological section images and in vivo mr images can ceritoglu, lei wang, lynn d.
Geodesic registration on anatomical manifolds github. Software national research resource for quantitative. The purpose of this paper is to establish singleparticipant white matter atlases based on diffusion tensor imaging. Software suites containing a variety of diffeomorphic mapping algorithms include the following. However, when two images differ greatly in shape, it is difficult to find a topologypreserving transformation algorithmically. As part of these efforts the center for imaging science at johns. Large deformation diffeomorphic metric mapping lddmm was intro.
This tutorial will contain the basic architecture of the software. One goal of computational anatomy ca is to develop tools to accurately segment brain structures in healthy and diseased subjects. The lddmm algorithm, introduced in faisal beg et al. Frontiers a fullyautomated subcortical and ventricular. The large deformation diffeomorphic metric mapping lddmm tool is an application which aims to assign metric distances on the space of anatomical images in computational anatomy thereby allowing for the direct comparison and quantization of morphometric changes in shapes. The large deformation diffeomorphic metric mapping lddmm tool is an application which aims to assign metric distances on the space of. The initial momentum parameterizes the whole geodesic and encodes the shape and form of the target image. In this paper, we evaluate the accuracy of normalization of dti data using a highly elastic transformation algorithm, called large deformation diffeomorphic metric mapping. The preconditioned update scheme is formulated for the nonstationary and the stationary parameterizations of diffeomorphisms, yielding three different lddmm methods. Diffeomorphic metric mapping of high angular resolution. Frontiers computational analysis of lddmm for brain mapping. Temporal subtraction of serial ct images with large. Hao huanga,b, can ceritogluc, xin lid, anqi qiuc,d, michael i.
218 1177 1430 1475 1106 1196 496 1136 1427 1208 462 325 667 602 1227 627 632 1068 355 1170 663 795 1479 115 56 1231 1089 1005 1419 622 817