Spatio-Temporal Shape Analysis of Cross-Sectional Data for Detection of Early Changes in Neurodegenerative Disease

Claire Cury 1, 2, 3 Marc Lorenzi 4 David Cash 5 Jennifer Nicholas 6 Alexandre Routier 2 Jonathan Rohrer 6 Sebastien Ourselin 7 Stanley Durrleman 8 Marc Modat 7
2 ARAMIS - Algorithms, models and methods for images and signals of the human brain
UPMC - Université Pierre et Marie Curie - Paris 6, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute, Inria de Paris
4 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
8 ARAMIS - Algorithms, models and methods for images and signals of the human brain
Inria Paris-Rocquencourt, UPMC - Université Pierre et Marie Curie - Paris 6, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
Abstract : The detection of pathological changes in neurodegenerative diseases that occur before clinical onset would be of great value for identifying suitable subjects and assessing drug ecacy in trials aimed at preventing or slowing onset. Using MRI derived volumetric information, researchers have been able to detect significant di↵erences between patients in the presymptomatic phase of neurodegenerative diseases and healthy controls. However, volumetric studies provide only a summary representation of complex morphological changes. Shape analysis has already been successfully applied to model pathological features in neu-rodegeneration and represents a valuable instrument to model presymp-tomatic anatomical changes occurring in specific brain regions. In this study we propose a computational framework to model group-wise spatio-temporal shape di↵erences, and to statistically evaluate the e↵ects of time and pathological components on the modeled variability. The proposed approach leverages the geodesic regression framework based on varifolds, and models the spatio-temporal shape variability via dimensionality reduction of the subject-specific " residual " transformations normalised in a common reference frame through parallel transport. The proposed approach is applied to patients with genetic variants of fronto-temporal dementia, and shows that shape di↵erences in the posterior part of the thalamus can be observed several years before the appearance of clinical symptoms.
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Claire Cury, Marc Lorenzi, David Cash, Jennifer Nicholas, Alexandre Routier, et al.. Spatio-Temporal Shape Analysis of Cross-Sectional Data for Detection of Early Changes in Neurodegenerative Disease. SeSAMI 2016 - First International Workshop Spectral and Shape Analysis in Medical Imaging, Sep 2016, Athens, Greece. pp.63 - 75, ⟨10.1007/978-3-319-51237-2_6⟩. ⟨hal-01440061⟩

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