SPM to the heart: mapping of 4D continuous velocities for motion abnormality quantification

International audienceThis paper proposes to apply parallel transport and statistical atlas techniques to quantify 4D myocardial motion abnormalities. We take advantage of our previous work on cardiac motion , which provided a continuous spatiotemporal representation of velocities, to interpolate and reorient cardiac motion fields to an unbiased reference space. Abnormal motion is quantified using SPM analysis on the velocity fields, which includes a correction based on random field theory to compensate for the spatial smoothness of the velocity fields. This paper first introduces the imaging pipeline for constructing a continuous 4D velocity atlas. This atlas is then applied to quantify abnormal motion patterns in heart failure patients

Atlas construction and image analysis using statistical cardiac models

International audienceThis paper presents a brief overview of current trends in the construction of population and multi-modal heart atlases in our group and their application to atlas-based cardiac image analysis. The technical challenges around the construction of these atlases are organized around two main axes: groupwise image registration of anatomical, motion and fiber images and construction of statistical shape models. Application-wise, this paper focuses on the extraction of atlas-based biomarkers for the detection of local shape or motion abnormalities, addressing several cardiac applications where the extracted information is used to study and grade different pathologies. The paper is concluded with a discussion about the role of statistical atlases in the integration of multiple information sources and the potential this can bring to in-silico simulations

Discrete Ladders for Parallel Transport in Transformation Groups with an Affine Connection Structure

International audienceModeling the temporal evolution of the tissues of the body is an important goal of medical image analysis, for instance for understanding the structural changes of organs affected by a pathology, or for studying the physiological growth during the life span. For such purposes we need to analyze and compare the observed anatomical differences between follow-up sequences of anatomical images of different subjects. Non-rigid registration is one of the main instruments for modeling anatomical differences from images. The aim of non-rigid registration is to encode the observed structural changes as deformation fields of the image space, which represent the warping required to match observed differences. This way, anatomical changes can be modeled and quantified by analyzing the associated deformations. The comparison of temporal evolutions thus requires the transport (or "normalization") of longitudinal deformations in a common reference frame. Normalization of longitudinal deformations can be done in different ways, depending on the feature of interest. For instance, local volume changes encoded by the scalar Jacobian determinant of longitudinal deformations can be compared by scalar resampling in a common reference frame via inter-subject registration. However, if we consider vector-valued deformation trajectories instead of scalar quantities, the transport is not uniquely defined anymore. Among the different normalization methods for deformation trajectories, the parallel transport is a powerful and promising tool which can be used within the ''diffeomorphic registration'' setting. Mathematically, parallel transporting a vector along a curve consists in translating it across the tangent spaces to the curve by preserving its parallelism according to a given derivative operation called (affine) connection. This chapter focuses on explicitly discrete algorithms for parallel transporting diffeomorphic deformations. Schild's ladder is an efficient and simple method proposed in theoretical Physics for the parallel transport of vectors along geodesics paths by iterative construction of infinitesimal geodesics parallelograms on the manifold. The base vertices of the parallelogram are given by the initial tangent vector to be transported. By iteratively building geodesic diagonals along the path, Schild's Ladder computes the missing vertex which corresponds to the transported vector. In this chapter we first show that the Schild ladder can lead to an effective computational scheme for the parallel transport of diffeomorphic deformations parameterized by tangent velocity fields. Schild's ladder may be however inefficient for transporting longitudinal deformations from image time series of multiple time points, in which the computation of the geodesic diagonals is required several times. We propose therefore a new parallel transport method based on the Schild's ladder, the "pole ladder", in which the computation of geodesics diagonals is minimized. Differently from the Schild's ladder, the pole ladder is symmetric with respect to the baseline-to-reference frame geodesic. From the theoretical point of view, we show that the pole ladder is rigorously equivalent to the Schild's ladder when transporting along geodesics. From the practical point of view, we establish the computational advantages and demonstrate the effectiveness of this very simple method by comparing with standard methods of transport on simulated images with progressing brain atrophy. Finally, we illustrate its application to a clinical problem: the measurement of the longitudinal progression in Alzheimer's disease. Results suggest that an important gain in sensitivity could be expected in group-wise comparisons

Lagrangian matching invariants for fibred four-manifolds: I

In a pair of papers, we construct invariants for smooth four-manifolds equipped with `broken fibrations' - the singular Lefschetz fibrations of Auroux, Donaldson and Katzarkov - generalising the Donaldson-Smith invariants for Lefschetz fibrations. The `Lagrangian matching invariants' are designed to be comparable with the Seiberg-Witten invariants of the underlying four-manifold. They fit into a field theory which assigns Floer homology groups to fibred 3-manifolds. The invariants are derived from moduli spaces of pseudo-holomorphic sections of relative Hilbert schemes of points on the fibres, subject to Lagrangian boundary conditions. Part I is devoted to the symplectic geometry of these Lagrangians.Comment: 72 pages, 4 figures. v.2 - numerous small corrections and clarification

Mixed-effects shape models for estimating longitudinal changes in anatomy

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Parallel transport, a central tool in geometric statistics for computational anatomy: Application to cardiac motion modeling

International audienceTransporting the statistical knowledge regressed in the neighbourhood of a point to a different but related place (transfer learning) is important for many applications. In medical imaging, cardiac motion modelling and structural brain changes are two such examples: for a group-wise statistical analysis, subjectspecific longitudinal deformations need to be transported in a common template anatomy. In geometric statistics, the natural (parallel) transport method is defined by the integration of a Riemannian connection which specifies how tangent vectors are compared at neighbouring points. In this process, the numerical accuracy of the transport method is critical. Discrete methods based on iterated geodesic parallelograms inspired by Schild’s ladder were shown to be very efficient and apparently stable in practice. In this chapter, we show that ladder methods are actually second order schemes, even with numerically approximated geodesics. We also propose a new original algorithm to implement these methods in the context of the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework that endows the space of diffeomorphisms with a right-invariant RKHS metric. When applied to the motion modelling of the cardiac right ventricle under pressure or volume overload, the method however exhibits unexpected effects in the presence of very large volume differences between subjects. We first investigate an intuitive rescaling of the modulus after parallel transport to preserve the ejection fraction. The surprisingly simple scaling/volume relationship that we obtain suggests to decouples the volume change from the deformation directly within the LDMMM metric. The parallel transport of cardiac trajectories with this new metric now reveals statistical insights into the dynamics of each disease. This example shows that parallel transport could become a tool of choice for data-driven metric optimization

Méthodes numériques et statistiques pour l'analyse de trajectoire dans un cadre de geométrie Riemannienne.

This PhD proposes new Riemannian geometry tools for the analysis of longitudinal observations of neuro-degenerative subjects. First, we propose a numerical scheme to compute the parallel transport along geodesics. This scheme is efficient as long as the co-metric can be computed efficiently. Then, we tackle the issue of Riemannian manifold learning. We provide some minimal theoretical sanity checks to illustrate that the procedure of Riemannian metric estimation can be relevant. Then, we propose to learn a Riemannian manifold so as to model subject's progressions as geodesics on this manifold. This allows fast inference, extrapolation and classification of the subjects.Cette thèse porte sur l'élaboration d'outils de géométrie riemannienne et de leur application en vue de la modélisation longitudinale de sujets atteints de maladies neuro-dégénératives. Dans une première partie, nous prouvons la convergence d'un schéma numérique pour le transport parallèle. Ce schéma reste efficace tant que l'inverse de la métrique peut être calculé rapidement. Dans une deuxième partie, nous proposons l'apprentissage une variété et une métrique riemannienne. Après quelques résultats théoriques encourageants, nous proposons d'optimiser la modélisation de progression de sujets comme des géodésiques sur cette variété

Efficient Parallel Transport of Deformations in Time Series of Images: from Schild's to Pole Ladder

International audienceGroup-wise analysis of time series of images requires to compare longitudinal evolutions of images observed on different subjects. In medical imaging, longitudinal anatomical changes can be modeled thanks to non-rigid registration of follow-up images. The comparison of longitudinal trajectories requires the transport (or "normalization") of longitudinal deformations in a common reference frame. We previously proposed an effective computational scheme based on the Schild's ladder for the parallel transport of diffeomorphic deformations parameterized by tangent velocity fields, based on the construction of a geodesic parallelogram on a manifold. Schild's ladder may be however inefficient for transporting longitudinal deformations from image time series of multiple time points, in which the computation of the geodesic diagonals is required several times. We propose here a new algorithm, the pole ladder, in which one diagonal of the parallelogram is the baseline-to-reference frame geodesic. This drastically reduces the number of geodesics to compute. Moreover, differently from the Schild's ladder, the pole ladder is symmetric with respect to the baseline-to-reference frame geodesic. From the theoretical point of view, we show that the pole ladder is rigorously equivalent to the Schild's ladder when transporting along geodesics. From the practical point of view, we establish the computational advantages and demonstrate the effectiveness of this very simple method by comparing with standard methods of transport on simulated images with progressing brain atrophy. Finally, we illustrate its application to a clinical problem: the measurement of the longitudinal progression in Alzheimer's disease. Results suggest that an important gain in sensitivity could be expected in group-wise comparisons