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Registration, atlas estimation and variability analysis of white matter fiber bundles modeled as currents

NeuroImage (Orlando, Fla.), 2011-04, Vol.55 (3), p.1073-1090 [Peer Reviewed Journal]

2010 Elsevier Inc. ;Copyright © 2010 Elsevier Inc. All rights reserved. ;Copyright Elsevier Limited Apr 1, 2011 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1053-8119 ;EISSN: 1095-9572 ;DOI: 10.1016/j.neuroimage.2010.11.056 ;PMID: 21126594

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Title:
Registration, atlas estimation and variability analysis of white matter fiber bundles modeled as currents
Author: Durrleman, Stanley ; Fillard, Pierre ; Pennec, Xavier ; Trouvé, Alain ; Ayache, Nicholas
Subjects: Algorithms ; Alzheimers disease ; Atlas construction ; Atlases as Topic ; Brain - anatomy & histology ; Brain - cytology ; Brain Mapping ; Computational anatomy ; Computer Science ; Currents ; Data Interpretation, Statistical ; Diffeomorphic registration ; Diffusion imaging ; Fiber bundle ; Fiber tract ; Humans ; Image Enhancement - methods ; Image Processing, Computer-Assisted - methods ; Imaging, Three-Dimensional - methods ; Leather & leather products ; Linear Models ; Medical Imaging ; Methods ; Models, Neurological ; Nerve Fibers, Myelinated - physiology ; Pyramidal Tracts - anatomy & histology ; Pyramidal Tracts - cytology ; Registration ; Statistical analysis ; Variability analysis
Is Part Of: NeuroImage (Orlando, Fla.), 2011-04, Vol.55 (3), p.1073-1090
Description: This paper proposes a generic framework for the registration, the template estimation and the variability analysis of white matter fiber bundles extracted from diffusion images. This framework is based on the metric on currents for the comparison of fiber bundles. This metric measures anatomical differences between fiber bundles, seen as global homologous structures across subjects. It avoids the need to establish correspondences between points or between individual fibers of different bundles. It can measure differences both in terms of the geometry of the bundles (like its boundaries) and in terms of the density of fibers within the bundle. It is robust to fiber interruptions and reconnections. In addition, a recently introduced sparse approximation algorithm allows us to give an interpretable representation of the fiber bundles and their variations in the framework of currents. First, we used this metric to drive the registration between two sets of homologous fiber bundles of two different subjects. A dense deformation of the underlying white matter is estimated, which is constrained by the bundles seen as global anatomical landmarks. By contrast, the alignment obtained from image registration is driven only by the local gradient of the image. Second, we propose a generative statistical model for the analysis of a collection of homologous bundles. This model consistently estimates prototype fiber bundles (called template), which capture the anatomical invariants in the population, a set of deformations, which align the geometry of the template to that of each subject and a set of residual perturbations. The statistical analysis of both the deformations and the residuals describe the anatomical variability in terms of geometry (stretching, torque, etc.) and “texture” (fiber density, etc.). Third, this statistical modeling allows us to simulate new synthetic bundles according to the estimated variability. This gives a way to interpret the anatomical features that the model detects consistently across the subjects. This may be used to better understand the bias introduced by the fiber extraction methods and eventually to give anatomical characterization of the normal or pathological variability of fiber bundles. ► Fiber bundle registration in absence of correspondence between individual fibers. ► Template estimation from a collection of homologous fiber bundles. ► Statistical analysis of both the geometry and the fiber density of the bundles.
Publisher: United States: Elsevier Inc
Language: English
Identifier: ISSN: 1053-8119
EISSN: 1095-9572
DOI: 10.1016/j.neuroimage.2010.11.056
PMID: 21126594
Source: ProQuest One Psychology
MEDLINE
Alma/SFX Local Collection
ProQuest Central

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Registration, atlas estimation and variability analysis of white matter fiber bundles modeled as currents

2010 Elsevier Inc. ;Copyright © 2010 Elsevier Inc. All rights reserved. ;Copyright Elsevier Limited Apr 1, 2011 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1053-8119 ;EISSN: 1095-9572 ;DOI: 10.1016/j.neuroimage.2010.11.056 ;PMID: 21126594

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