The CONNECT project: Combining macro- and micro-structure.

In recent years, diffusion MRI has become an extremely important tool for studying the morphology of living brain tissue, as it provides unique insights into both its macrostructure and microstructure. Recent applications of diffusion MRI aimed to characterize the structural connectome using tractography to infer connectivity between brain regions. In parallel to the development of tractography, additional diffusion MRI based frameworks (CHARMED, AxCaliber, ActiveAx) were developed enabling the extraction of a multitude of micro-structural parameters (axon diameter distribution, mean axonal diameter and axonal density). This unique insight into both tissue microstructure and connectivity has enormous potential value in understanding the structure and organization of the brain as well as providing unique insights to abnormalities that underpin disease states. The CONNECT (Consortium Of Neuroimagers for the Non-invasive Exploration of brain Connectivity and Tracts) project aimed to combine tractography and micro-structural measures of the living human brain in order to obtain a better estimate of the connectome, while also striving to extend validation of these measurements. This paper summarizes the project and describes the perspective of using micro-structural measures to study the connectome.

Deformable modeling using a 3D boundary representation with quadratic constraints on the branching structure of the Blum skeleton.

We propose a new approach for statistical shape analysis of 3D anatomical objects based on features extracted from skeletons. Like prior work on medial representations, the approach involves deforming a template to target shapes in a way that preserves the branching structure of the skeleton and provides intersubject correspondence. However, unlike medial representations, which parameterize the skeleton surfaces explicitly, our representation is boundary-centric, and the skeleton is implicit. Similar to prior constrained modeling methods developed 2D objects or tube-like 3D objects, we impose symmetry constraints on tuples of boundary points in a way that guarantees the preservation of the skeleton's topology under deformation. Once discretized, the problem of deforming a template to a target shape is formulated as a quadratically constrained quadratic programming problem. The new technique is evaluated in terms of its ability to capture the shape of the corpus callosum tract extracted from diffusion-weighted MRI.

Beyond crossing fibers: tractography exploiting sub-voxel fibre dispersion and neighbourhood structure.

In this paper we propose a novel algorithm which leverages models of white matter fibre dispersion to improve tractography. Tractography methods exploit directional information from diffusion weighted magnetic resonance (DW-MR) imaging to infer connectivity between different brain regions. Most tractography methods use a single direction (e.g. the principal eigenvector of the diffusion tensor) or a small set of discrete directions (e.g. from the peaks of an orientation distribution function) to guide streamline propagation. This strategy ignores the effects of within-bundle orientation dispersion, which arises from fanning or bending at the sub-voxel scale, and can lead to missing connections. Various recent DW-MR imaging techniques estimate the fibre dispersion in each bundle directly and model it as a continuous distribution. Here we introduce an algorithm to exploit this information to improve tractography. The algorithm further uses a particle filter to probe local neighbourhood structure during streamline propagation. Using information gathered from neighbourhood structure enables the algorithm to resolve ambiguities between converging and diverging fanning structures, which cannot be distinguished from isolated orientation distribution functions. We demonstrate the advantages of the new approach in synthetic experiments and in vivo data. Synthetic experiments demonstrate the effectiveness of the particle filter in gathering and exploiting neighbourhood information in recovering various canonical fibre configurations and experiments with in vivo brain data demonstrate the advantages of utilising dispersion in tractography, providing benefits in practical situations.