To obtain an accurate representation of a brain structural connectivity, diffusion MRI and fiber tracking depend on a good understanding of white matter fiber structures. Although the tracking methods work well when performed in single orientation fiber bundles, most methods are limited in more complex cases, especially to take into account crossing, fanning, and kissing fibers. A recent international fiber tracking challenge concluded that most tracking algorithms generated 4–5 times more false positive tracks than true tracks on average. This was attributed in large part to a lack of knowledge about the fiber crossing geometry. There is thus a dire need to study more complex fiber geometries to improve the tractography algorithms, for example by classifying those geometries into characteristic crossing topologies (e.g., fanning, curving, bottleneck, pure crossing, ...). Here, we propose a multimodal neuroimaging pipeline to identify and acquire fiber crossing areas in whole mouse brains. Our method uses the Allen Mouse Brain connectivity atlas and tractogram analysis using diffusion MRI techniques to identify candidate regions of interests containing fiber crossings based on two predetermined retrograde viral injection site locations. Based on serial OCT acquisitions, we confirmed the location of crossings. Further experiments will validate in detail the structural nature of crossings using retrograde injections of fluorescent tracers and whole mouse brain serial blockface histology. We believe that this new methodological approach will provide indispensable data for the development of a new generation of tractography algorithms that better resolve complex fiber geometries.
An automated dual-resolution serial optical coherence tomography (2R-SOCT) scanner is developed. The serial histology system combines a low-resolution (25 μm / voxel) 3 × OCT with a high-resolution (1.5 μm / voxel) 40 × OCT to acquire whole mouse brains at low resolution and to target specific regions of interest (ROIs) at high resolution. The 40 × ROIs positions are selected either manually by the microscope operator or using an automated ROI positioning selection algorithm. Additionally, a multimodal and multiresolution registration pipeline is developed in order to align the 2R-SOCT data onto diffusion MRI (dMRI) data acquired in the same ex vivo mouse brains prior to automated histology. Using this imaging system, 3 whole mouse brains are imaged, and 250 high-resolution 40 × three-dimensional ROIs are acquired. The capability of this system to perform multimodal imaging studies is demonstrated by labeling the ROIs using a mouse brain atlas and by categorizing the ROIs based on their associated dMRI measures. This reveals a good correspondence of the tissue microstructure imaged by the high-resolution OCT with various dMRI measures such as fractional anisotropy, number of fiber orientations, apparent fiber density, orientation dispersion, and intracellular volume fraction.
An automated massive histology setup combined with an optical coherence tomography (OCT) microscope was used to image a total of n=5 whole mouse brains. Each acquisition generated a dataset of thousands of OCT volumetric tiles at a sampling resolution of 4.9×4.9×6.5 μm. This paper describes techniques for reconstruction and segmentation of the sliced brains. In addition to the measured OCT optical reflectivity, a single scattering photon model was used to compute the attenuation coefficients within each tissue slice. Average mouse brain templates were generated for both the OCT reflectivity and attenuation contrasts and were used with an n-tissue segmentation algorithm. To better understand the brain tissue OCT contrast origin, one of the mouse brains was acquired using dMRI and coregistered to its corresponding assembled brain. Our results indicate that the optical reflectivity in a fiber bundle varies with its orientation, its fiber density, and the number of fiber orientations it contains. The OCT mouse brain template generation and coregistration to dMRI data demonstrate the potential of this massive histology technique to pursue cross-sectional, multimodal, and multisubject investigations of small animal brains.
Diffusion Tensor Imaging (DTI) is currently a widespread technique to infer white matter architecture in the
human brain. An important application of DTI is to understand the anatomical coupling between functional
cortical regions of the brain. To solve this problem, anisotropy maps are insufficient and fiber tracking methods
are used to obtain the main fibers. While the diffusion tensor (DT) is important to obtain anisotropy maps
and apparent diffusivity of the underlying tissue, fiber tractography using the full DT may result in diffusive
tracking that leaks into unexpected regions. Sharpening is thus of utmost importance to obtain complete and
accurate tracts. In the tracking literature, only heuristic methods have been proposed to deal with this problem.
We propose a new tensor sharpening transform. Analogously to the general issue with the diffusion and fiberOrientation Distribution Function (ODF) encountered when working with High Angular Resolution Diffusion
Imaging (HARDI), we show how to transform the diffusion tensors into so-called fiber tensors. We demonstrate
that this tensor transform is a natural pre-processing task when one is interested in fiber tracking. It also leads
to a dramatic improvement of the tractography results obtained by front propagation techniques on the full
diffusion tensor. We compare and validate sharpening and tracking results on synthetic data and on known fiber
bundles in the human brain.
High angular resolution diffusion imaging (HARDI) has recently been of great interest to characterize non-Gaussian diffusion process. In the white matter of the brain, this occurs when fiber bundles cross, kiss or diverge within the same voxel. One of the important goal is to better describe the apparent diffusion process in these multiple fiber regions, thus overcoming the limitations of classical diffusion tensor imaging (DTI). In this paper, we design the appropriate mathematical tools to describe noisy HARDI data. Using a meaningful modified spherical harmonics basis to capture the physical constraints of the problem, we propose a new regularization algorithm to estimate a smoother and closer diffusivity profile to the true diffusivities without noise. We exploit properties of the spherical harmonics to define a smoothing term based on the Laplace-Beltrami for functions defined on the unit sphere. An additional contribution of the paper is the derivation of the general transformation taking the spherical harmonics coefficients to the high order tensor independent elements. This allows the careful study of the state of the art high order anisotropy measures computed from either spherical harmonics or tensor coefficients. We analyze their ability to characterize the underlying diffusion process. We are able to recover voxels with isotropic, single fiber anisotropic and multiple fiber anisotropic diffusion. We test and validate the approach on diffusion profiles from synthetic data and from a biological rat phantom.
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