Manifold learning characterization of abnormal myocardial motion patterns: application to CRT-induced changes

International audienceThe present paper aims at quantifying the evolution of a given motion pattern under cardiac resynchronization therapy (CRT). It builds upon techniques for population-based cardiac motion quantifica-tion (statistical atlases, for inter-sequence spatiotemporal alignment and the definition of normal/abnormal motion). Manifold learning is used on spatiotemporal maps of myocardial motion abnormalities to represent a given abnormal pattern and to compare any individual to that pattern. The methodology was applied to 2D echocardiographic sequences in a 4-chamber view from 108 subjects (21 healthy volunteers and 87 CRT candidates) at baseline, with pacing ON, and at 12 months follow-up. Experiments confirmed that recovery of a normal motion pattern is a necessary but not su cient condition for CRT response

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

Local and Global Energies for Shape Analysis in Medical Imaging

In a previous contribution a new Riemannian shape space, named TPS space, was introduced to perform statistics on shape data. This space was endowed with a Rie-mannian metric and a flat connection, with torsion, compatible with the given metric. This connection allows the definition of a Parallel Transport of the deformation compatible with the threefold decomposition in spherical, deviatoric and non affine components. Such a Parallel Transport also conserves the-energy, strictly related to the total elastic strain energy stored by the body in the original deformation. New machinery is here presented in order to calculate the bending energy on the body only (body bending energy) in order to restrict it exclusively within physical boundaries of objects involved in the deformation analysis. The novelty of this new procedure resides in the fact that we propose a new metric to conserve during the TPS direct transport. This allows transporting the shape change more coherently with the mechanical meaning of the deformation. The geometry of the TPS Space is then further developed in order to better represent the relationship between the-energy, the strain energy and the so called bending-energy densities

Partial Differential Equation-Constrained Diffeomorphic Registration from Sum of Squared Differences to Normalized Cross-Correlation, Normalized Gradient Fields, and Mutual Information: A Unifying Framework; 35632143

This work proposes a unifying framework for extending PDE-constrained Large Deformation Diffeomorphic Metric Mapping (PDE-LDDMM) with the sum of squared differences (SSD) to PDE-LDDMM with different image similarity metrics. We focused on the two best-performing variants of PDE-LDDMM with the spatial and band-limited parameterizations of diffeomorphisms. We derived the equations for gradient-descent and Gauss-Newton-Krylov (GNK) optimization with Normalized Cross-Correlation (NCC), its local version (lNCC), Normalized Gradient Fields (NGFs), and Mutual Information (MI). PDE-LDDMM with GNK was successfully implemented for NCC and lNCC, substantially improving the registration results of SSD. For these metrics, GNK optimization outperformed gradient-descent. However, for NGFs, GNK optimization was not able to overpass the performance of gradient-descent. For MI, GNK optimization involved the product of huge dense matrices, requesting an unaffordable memory load. The extensive evaluation reported the band-limited version of PDE-LDDMM based on the deformation state equation with NCC and lNCC image similarities among the best performing PDE-LDDMM methods. In comparison with benchmark deep learning-based methods, our proposal reached or surpassed the accuracy of the best-performing models. In NIREP16, several configurations of PDE-LDDMM outperformed ANTS-lNCC, the best benchmark method. Although NGFs and MI usually underperformed the other metrics in our evaluation, these metrics showed potentially competitive results in a multimodal deformable experiment. We believe that our proposed image similarity extension over PDE-LDDMM will promote the use of physically meaningful diffeomorphisms in a wide variety of clinical applications depending on deformable image registration

Network Analysis: Applications for the Developing Brain

Development of the human brain follows a complex trajectory of age-specific anatomical and physiological changes. The application of network analysis provides an illuminating perspective on the dynamic interregional and global properties of this intricate and complex system. Here, we provide a critical synopsis of methods of network analysis with a focus on developing brain networks. After discussing basic concepts and approaches to network analysis, we explore the primary events of anatomical cortical development from gestation through adolescence. Upon this framework, we describe early work revealing the evolution of age-specific functional brain networks in normal neurodevelopment. Finally, we review how these relationships can be altered in disease and perhaps even rectified with treatment. While this method of description and inquiry remains in early form, there is already substantial evidence that the application of network models and analysis to understanding normal and abnormal human neural development holds tremendous promise for future discovery

AGE-RELATED VULNERABILITIES ALONG THE HIPPOCAMPAL LONGITUDINAL AXIS

Master'sMASTER OF ENGINEERIN

The decomposition of deformation: new metrics to enhance shape analysis in medical imaging

In landmarks-based Shape Analysis size is measured, in most cases, with Centroid Size. Changes in shape are decomposed in affine and non affine components. Furthermore the non affine component can be in turn decomposed in a series of local deformations (partial warps). If the extent of deformation between two shapes is small, the difference between centroid size and m-Volume increment is barely appreciable. In medical imaging applied to soft tissues bodies can undergo very large deformations, involving large changes in size. The cardiac example, analyzed in the present paper, shows changes in m-Volume that can reach the 60%. We show here that standard Geometric Morphometrics tools (landmarks, Thin Plate Spline, and related decomposition of the deformation) can be generalized to better describe the very large deformations of biological tissues, without losing a synthetic description. In particular, the classical decomposition of the space tangent to the shape space in affine and non affine components is enriched to include also the change in size, in order to give a complete description of the tangent space to the size-and-shape space. The proposed generalization is formulated by means of a new Riemannian metric describing the change in size as change in m-Volume rather than change in Centroid Size. This leads to a redefinition of some aspects of the Kendall’s size-and-shape space without losing Kendall’s original formulation. This new formulation is discussed by means of simulated examples using 2D and 3D platonic shapes as well as a real example from clinical 3D echocardiographic data. We demonstrate that our decomposition based approaches discriminate very effectively healthy subjects from patients affected by Hypertrophic Cardiomyopathy

Advanced Methods for Discovering Genetic Markers Associated with High Dimensional Imaging Data

Imaging genetic studies have been widely applied to discover genetic factors of inherited neuropsychiatric diseases. Despite the notable contribution of genome-wide association studies (GWAS) in neuroimaging research, it has always been difficult to efficiently perform association analysis on imaging phenotypes. There are several challenges arising from this topic, such as the large dimensionality of imaging data and genetic data, the potential spatial dependency of imaging phenotypes and the computational burden of the GWAS problem. All the aforementioned issues motivate us to investigate new statistical methods in neuroimaging genetic analysis. In the first project, we develop a hierarchical functional principal regression model (HFPRM) to simultaneously study diffusion tensor bundle statistics on multiple fiber tracts. Theoretically, the asymptotic distribution of the global test statistic on the common factors has been studied. Simulations are conducted to evaluate the finite sample performance of HFPRM. Finally, we apply our method to a GWAS of a neonate population to explore important genetic architecture in early human brain development. In the second project, we consider an association test between functional data acquired on a single curve and scalar variables in a varying coefficient model. We propose a functional projection regression model and an associated global test statistic to aggregate weak signals across the domain of functional data. Theoretically, we examine the asymptotic distribution of the global test statistic and provide a strategy to adaptively select the tuning parameter. Simulation experiments show that the proposed test outperforms existing state-of-the-art methods in functional statistical inference. We also apply the proposed method to a GWAS in the UK Biobank dataset. In the third project, we introduce an adaptive projection regression model (APRM) to perform statistical inference on high dimensional imaging responses in the presence of high correlations. Dimension reduction of the phenotypes is achieved through a linear projection regression model. We also implement an adaptive inference procedure to detect signals at multiple levels. Numerical simulations demonstrate that APRM outperforms many state-of-the-art methods in high dimensional inference. Finally, we apply APRM to a GWAS of volumetric data on 93 regions of interest in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.Doctor of Philosoph