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

Durrleman, Stanley; Fillard, Pierre; Pennec, Xavier; Trouvé, Alain; Ayache, Nicholas

doi:10.1016/j.neuroimage.2010.11.056

Cited by 84 publications

(92 citation statements)

References 48 publications

(61 reference statements)

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“…Rather inter-individual differences (inter-subject variability) is an important topic of research as it may contain important information, for example for a certain cognitive experience or ability (Schlaug et al, 1995). In fact, relating inter-individual differences in brain structure to behavioral phenotype may represent a most powerful approach to inferring functional correlates of inter-personally variable anatomy (e.g., Durrleman et al, 2011;Thiebaut de Schotten et al, 2011). Following the notion that inter-subject variability may be a key component of understanding brain organization, most current brain atlases are probabilistic by their very nature (e.g., Forkert et al, 2012;Mazziotta et al, 2001;Roland and Zilles, 1994;Sun et al, 2012) and hence reflect this important aspect of brain organization.…”

Section: Heterogeneity and Variability Of Featuresmentioning

confidence: 99%

“…Conversely, the construction of white matter atlases based (primarily) on diffusion-weighted imaging (e.g., Durrleman et al, 2011;Oishi et al, 2008;Prasad et al, 2014;Thiebaut de Schotten et al, 2011;Zhang et al, 2010), their integration with those representing gray matter features or cross-species comparisons (Dougherty et al, 2005;Jbabdi et al, 2013;Javad et al, 2014;Sallet et al, 2013;Thiebaut de Schotten et al, 2012;Yendiki et al, 2011) will not be in the focus of the present work. We hope that this constraint will allow us to provide a more coherent overview on the state of the field, the challenges towards a true multi-modal brain atlas and potential solutions to overcome these.…”

Section: Introductionmentioning

confidence: 99%

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Interoperable atlases of the human brain

Amunts

Hawrylycz²,

Essen³

et al. 2014

NeuroImage

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The last two decades have seen an unprecedented development of human brain mapping approaches at various spatial and temporal scales. Together, these have provided a large fundus of information on many different aspects of the human brain including micro-and macrostructural segregation, regional specialization of function, connectivity, and temporal dynamics. Atlases are central in order to integrate such diverse information in a topographically meaningful way. It is noteworthy, that the brain mapping field has been developed along several major lines such as structure vs. function, postmortem vs. in vivo, individual features of the brain vs. population-based aspects, or slow vs. fast dynamics. In order to understand human brain organization, however, it seems inevitable that these different lines are integrated and combined into a multimodal human brain model. To this aim, we held a workshop to determine the constraints of a multi-modal human brain model that are needed to enable (i) an integration of different spatial and temporal scales and data modalities into a common reference system, and (ii) efficient data exchange and analysis. As detailed in this report, to arrive at fully interoperable atlases of the human brain will still require much work at the frontiers of data acquisition, analysis, and representation. Among them, the latter may provide the most challenging task, in particular when it comes to representing features of vastly different scales of space, time and abstraction. The potential benefits of such endeavor, however, clearly outweigh the problems, as only such kind of multi-modal human brain atlas may provide a starting point from which the complex relationships between structure, function, and connectivity may be explored.

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Section: Heterogeneity and Variability Of Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interoperable atlases of the human brain

Amunts

Hawrylycz²,

Essen³

et al. 2014

NeuroImage

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“…Here we propose a novel dissimilarity measure which considers both the pathway and the anatomical locations of the termini of the fibers. It can be seen as an extension of the framework of currents [3] and we have called it: weighted currents. As usual currents, it does not need point-to-point correspondence between fibers, it has a closed form and easy to compute expression and it can be used to compare fiber bundles.…”

Section: Introductionmentioning

confidence: 99%

A Prototype Representation to Approximate White Matter Bundles with Weighted Currents

Gori

Colliot

Marrakchi-Kacem

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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Abstract. Quantitative and qualitative analysis of white matter fibers resulting from tractography algorithms is made difficult by their huge number. To this end, we propose an approximation scheme which gives as result a more concise but at the same time exhaustive representation of a fiber bundle. It is based on a novel computational model for fibers, called weighted currents, characterised by a metric that considers both the pathway and the anatomical locations of the endpoints of the fibers. Similarity has therefore a twofold connotation: geometrical and related to the connectivity. The core idea is to use this metric for approximating a fiber bundle with a set of weighted prototypes, chosen among the fibers, which represent ensembles of similar fibers. The weights are related to the number of fibers represented by the prototypes. The algorithm is divided into two steps. First, the main modes of the fiber bundle are detected using a modularity based clustering algorithm. Second, a prototype fiber selection process is carried on in each cluster separately. This permits to explain the main patterns of the fiber bundle in a fast and accurate way.

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“…In the present state-of-the-art, the concept of geodesic shape averaging allows unbiased constructions of atlases through diffeomorphic methods [12,2,17], i.e., the transformation of a reference shape toward an average (the geometry of the atlas) follows a geodesic path on a Riemannian manifold (the space of diffeomorphic transformations). While the LDDMM [4,3,6] or forward scheme approaches [1,8] provide elegant mathematical frameworks for averaging shapes, these methods could be slow and find their limitations with high shape variability. Guimond et al [10] proposed a fast and efficient algorithm [19,16,26] with sequential (pairwise) registrations to a reference image.…”

Section: Introductionmentioning

confidence: 99%

Groupwise Spectral Log-Demons Framework for Atlas Construction

Lombaert

Grady

Pennec

et al. 2013

Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging

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Abstract. We introduce a new framework to construct atlases from images with very large and complex deformations. The atlas is build in parallel with groupwise registrations by extending the symmetric Log-Demons algorithm. We describe and evaluate two forms of our framework: the Groupwise LogDemons (GL-Demons) is faster but is limited to local nonrigid deformations, and the Groupwise Spectral Log-Demons (GSL-Demons) is slower but, due to isometry-invariant representations of images, can construct atlases of organs with high shape variability. We demonstrate our framework by constructing atlases from hearts with high shape variability.

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

Cited by 84 publications

References 48 publications

Interoperable atlases of the human brain

Interoperable atlases of the human brain

A Prototype Representation to Approximate White Matter Bundles with Weighted Currents

Groupwise Spectral Log-Demons Framework for Atlas Construction

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