Abstract: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… Show more
“…In order to deal with the considerable amount of fibers resulting from tractography algorithms, we rely on the approximation scheme introduced in [4]. Fiber bundles are approximated with weighted prototypes represented as "tubes".…”
Section: Introductionmentioning
confidence: 99%
“…The templates of cortex and putamen have been initialised as the average of the vertices. For the fiber bundle template, we have first gathered the fibers of all subjects in a single bundle which has then been approximated as a set of weighted prototypes [4]. Both Fig.3 and Fig.4 show the first mode of PCA based on the estimated covariance matrix of the deformation parameters of the first diffeomorphism W applied to the fiber bundle template keeping fixed the templates of cortex and putamen.…”
Section: Optimization Proceduresmentioning
confidence: 99%
“…Weighted Prototypes A fiber bundle B is approximated with a set of weighted prototypes {τ i M i } chosen among the fibers [4]. The prototype M i is modelled as a weighted current and its weight τ i is linked to the number of fibers approximated.…”
Section: Object Representationmentioning
confidence: 99%
“…The norm of the difference between two meshes is defined as the sum of squared differences between pair of vertices. White matter Fiber bundles are modelled as weighted currents [4]. Let X and Y be two fibers which can be modelled as polygonal lines of Q and Z segments respectively.…”
Section: Object Representationmentioning
confidence: 99%
“…They are chosen among the fibers and their radius is related to the number of fibers approximated. This new representation is based on the metric of weighted currents [4], an extension of the framework of currents. As usual currents, it does not require point-correspondence between fibers or fiber-correspondence between bundles.…”
Abstract. This work proposes an atlas construction method to jointly analyse the relative position and shape of fiber tracts and gray matter structures. It is based on a double diffeomorphism which is a composition of two diffeomorphisms. The first diffeomorphism acts only on the white matter keeping fixed the gray matter of the atlas. The resulting white matter, together with the gray matter, are then deformed by the second diffeomorphism. The two diffeomorphisms are related and jointly optimised. In this way, the first diffeomorphisms explain the variability in structural connectivity within the population, namely both changes in the connected areas of the gray matter and in the geometry of the pathway of the tracts. The second diffeomorphisms put into correspondence the homologous anatomical structures across subjects. Fiber bundles are approximated with weighted prototypes using the metric of weighted currents. The atlas, the covariance matrix of deformation parameters and the noise variance of each structure are automatically estimated using a Bayesian approach. This method is applied to patients with Tourette syndrome and controls showing a variability in the structural connectivity of the left cortico-putamen circuit.
“…In order to deal with the considerable amount of fibers resulting from tractography algorithms, we rely on the approximation scheme introduced in [4]. Fiber bundles are approximated with weighted prototypes represented as "tubes".…”
Section: Introductionmentioning
confidence: 99%
“…The templates of cortex and putamen have been initialised as the average of the vertices. For the fiber bundle template, we have first gathered the fibers of all subjects in a single bundle which has then been approximated as a set of weighted prototypes [4]. Both Fig.3 and Fig.4 show the first mode of PCA based on the estimated covariance matrix of the deformation parameters of the first diffeomorphism W applied to the fiber bundle template keeping fixed the templates of cortex and putamen.…”
Section: Optimization Proceduresmentioning
confidence: 99%
“…Weighted Prototypes A fiber bundle B is approximated with a set of weighted prototypes {τ i M i } chosen among the fibers [4]. The prototype M i is modelled as a weighted current and its weight τ i is linked to the number of fibers approximated.…”
Section: Object Representationmentioning
confidence: 99%
“…The norm of the difference between two meshes is defined as the sum of squared differences between pair of vertices. White matter Fiber bundles are modelled as weighted currents [4]. Let X and Y be two fibers which can be modelled as polygonal lines of Q and Z segments respectively.…”
Section: Object Representationmentioning
confidence: 99%
“…They are chosen among the fibers and their radius is related to the number of fibers approximated. This new representation is based on the metric of weighted currents [4], an extension of the framework of currents. As usual currents, it does not require point-correspondence between fibers or fiber-correspondence between bundles.…”
Abstract. This work proposes an atlas construction method to jointly analyse the relative position and shape of fiber tracts and gray matter structures. It is based on a double diffeomorphism which is a composition of two diffeomorphisms. The first diffeomorphism acts only on the white matter keeping fixed the gray matter of the atlas. The resulting white matter, together with the gray matter, are then deformed by the second diffeomorphism. The two diffeomorphisms are related and jointly optimised. In this way, the first diffeomorphisms explain the variability in structural connectivity within the population, namely both changes in the connected areas of the gray matter and in the geometry of the pathway of the tracts. The second diffeomorphisms put into correspondence the homologous anatomical structures across subjects. Fiber bundles are approximated with weighted prototypes using the metric of weighted currents. The atlas, the covariance matrix of deformation parameters and the noise variance of each structure are automatically estimated using a Bayesian approach. This method is applied to patients with Tourette syndrome and controls showing a variability in the structural connectivity of the left cortico-putamen circuit.
This paper introduces the use of unbalanced optimal transport methods as a similarity measure for diffeomorphic matching of imaging data. The similarity measure is a key object in diffeomorphic registration methods that, together with the regularization on the deformation, defines the optimal deformation. Most often, these similarity measures are local or non local but simple enough to be computationally fast. We build on recent theoretical and numerical advances in optimal transport to propose fast and global similarity measures that can be used on surfaces or volumetric imaging data. This new similarity measure is computed using a fast generalized Sinkhorn algorithm. We apply this new metric in the LDDMM framework on synthetic and real data, fibres bundles and surfaces and show that better matching results are obtained.
The extraction of fibers from dMRI data typically produces a large number of fibers, it is common to group fibers into bundles. To this end, many specialized distance measures, such as MCP, have been used for fiber similarity. However, these distance based approaches require point-wise correspondence and focus only on the geometry of the fibers. Recent publications have highlighted that using microstructure measures along fibers improves tractography analysis. Also, many neurodegenerative diseases impacting white matter require the study of microstructure measures as well as the white matter geometry. Motivated by these, we propose to use a novel computational model for fibers, called functional varifolds, characterized by a metric that considers both the geometry and microstructure measure (e.g. GFA) along the fiber pathway. We use it to cluster fibers with a dictionary learning and sparse coding-based framework, and present a preliminary analysis using HCP data.
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