Comparison of the endocast growth of

Abstract. The study of neurodegenerative pathologies like Alzheimer’s disease led to an increasing interest in the evaluation of the morphological changes in the brain over time. The recent availability of public longitudinal datasets requires new approaches to consistently measure the changes through sequences of MR images of a specific subject. Nonrigid registration represents an instrument to measure atrophy as geometric differences between pairs of scans. Among these methods, the Symmetric Log-Demons algorithm is a computationally efficient registration algorithm which defines the transformations as diffeomorphisms. In this work we propose a robust framework for the intra-subject nonrigid registration of serial MR images to evaluate the brain changes in time. The temporal consistency is obtained by integration of the structural changes at each time point into a 4-dimensional warping algorithm, to describe the subject-specific temporal trajectory. Moreover, we will show how to derive measurements of brain changes consistently along the spatial dimension, from the voxel to the regional level. Results on synthetic and real data show that, under this approach, the resulting deformations define smoother trajectories for the evolution of the changes. The accuracy of the measurements is also improved by reducing the influence of intrasubject variability and the biases affecting the data. The present method could represent the basis for the development of a robust and consistent model of longitudinal changes at the population level. 1

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Abstract—Cardiac remodelling plays a crucial role in heart diseases. Analysing how the heart grows and remodels over time can provide precious insights into pathological mechanisms, eventually resulting in quantitative metrics for disease evaluation and therapy planning. This study aims to quantify the regional impacts of valve regurgitation and heart growth upon the enddiastolic right ventricle (RV) in patients with tetralogy of Fallot, a severe congenital heart defect. The ultimate goal is to determine, among clinical variables, predictors for the RV shape from which a statistical model that predicts RV remodelling is built. Our approach relies on a forward model based on currents and a diffeomorphic surface registration algorithm to estimate an unbiased template. Local effects of RV regurgitation upon the RV shape were assessed with Principal Component Analysis (PCA) and cross-sectional multivariate design. A generative 3D model of RV growth was then estimated using partial least squares (PLS) and canonical correlation analysis (CCA). Applied on a retrospective population of 49 patients, cross-effects between growth and pathology could be identified. Qualitatively, the statistical findings were found realistic by cardiologists. 10-fold cross-validation demonstrated a promising generalisation and stability of the growth model. Compared to PCA regression, PLS was more compact, more precise and provided better predictions.

Abstract. The aim of computational anatomy is to develop models for understanding the physiology of organs and tissues. The diffeomorphic non-rigid registration is a validated instrument for the detection of anatomical changes on medical images and is based on a rich mathematical background. For instance, the “large deformation diffeomorphic metric mapping ” framework defines a Riemannian setting by providing an opportune right invariant metric on the tangent space, and solves the registration problem by computing geodesics parametrized by time-varying velocity fields. In alternative, stationary velocity fields have been proposed for the diffeomorphic registration based on the oneparameter subgroups from Lie groups theory. In spite of the higher computational efficiency, the geometric setting of the latter method is more vague, especially regarding the relationship between one-parameter subgroups and geodesics. In this study, we present the relevant properties of the Lie groups for the definition of geometrical properties within the one-parameter subgroups parametrization, and we define the geometric structure for computing geodesics and for parallel transporting. The theoretical results are applied to the image registration context, and discussed in light of the practical computational problems. 1

Longitudinal analysis of anatomical changes is a vital component in many personalized-medicine applications for predicting disease onset, determining growth/atrophy patterns, evaluating disease progression, and monitoring recovery. Estimating anatomical changes in longitudinal studies, especially through magnetic resonance (MR) images, is challenging because of temporal variability in shape (e.g. from growth/atrophy) and appearance (e.g. due to imaging parameters and tissue properties affecting intensity contrast, or from scanner calibration). This paper proposes a novel mathematical framework for constructing subject-specific longitudinal anatomical models. The proposed method solves a generalized problem of joint segmentation, registration, and subjectspecific atlas building, which involves not just two images, but an entire longitudinal image sequence. The proposed framework describes a novel approach that integrates fundamental principles that underpin methods for image segmentation, image registration, and atlas construction. This paper presents evaluation on simulated longitudinal data and on clinical longitudinal brain MRI data. The results demonstrate that the proposed framework effectively integrates information from 4-D spatiotemporal data to generate spatiotemporal models that allow analysis of anatomical changes over time. 1.

journal homepage: www.elsevier.com/locate/jhevol Comparison of the endocranial ontogenies between chimpanzees and bonobos

Abstract. Statistical analysis of longitudinal imaging data is crucial for understanding normal anatomical development as well as disease progression. This fundamental task is challenging due to the difficulty in modeling longitudinal changes, such as growth, and comparing changes across different populations. We propose a new approach for analyzing shape variability over time, and for quantifying spatiotemporal population differences. Our approach estimates 4D anatomical growth models for a reference population (an average model) and for individuals in different groups. We define a reference 4D space for our analysis as the average population model and measure shape variability through diffeomorphisms that map the reference to the individuals. Conducting our analysis on this 4D space enables straightforward statistical analysis of deformations as they are parameterized by momenta vectors that are located at homologous locations in space and time. We evaluate our methodonasyntheticshapedatabaseandclinicaldatafromastudythat seeks to quantify brain growth differences in infants at risk for autism. 1