Cortical thickness and surface area are important morphological measures with implications for many psychiatric and neurological conditions. Automated segmentation and reconstruction of the cortical surface from 3D MRI scans is challenging due to the variable anatomy of the cortex and its highly complex geometry. While many methods exist for this task in the context of the human brain, these methods are typically not readily applicable to the primate brain. We propose an innovative approach based on our recently proposed human cortical reconstruction algorithm, LOGISMOS-B, and the Laplace-based thickness measurement method.
Quantitative evaluation of our approach was performed based on a dataset of T1- and T2-weighted MRI scans from 12-month-old macaques where labeling by our anatomical experts was used as independent standard. In this dataset, LOGISMOS-B has an average signed surface error of 0.01 ± 0.03mm and an unsigned surface error of 0.42 ± 0.03mm over the whole brain.
Excluding the rather problematic temporal pole region further improves unsigned surface distance to 0.34 ± 0.03mm. This high level of accuracy reached by our algorithm even in this challenging developmental dataset illustrates its robustness and its potential for primate brain studies.
Purpose: The UNC-Utah NA-MIC DTI framework represents a coherent, open source, atlas fiber tract based DTI
analysis framework that addresses the lack of a standardized fiber tract based DTI analysis workflow in the field. Most
steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for
non-technical researchers/investigators. Data: We illustrate the use of our framework on a 54 directional DWI
neuroimaging study contrasting 15 Smokers and 14 Controls. Method(s): At the heart of the framework is a set of tools anchored around the multi-purpose image analysis platform 3D-Slicer. Several workflow steps are handled via external modules called from Slicer in order to provide an integrated approach. Our workflow starts with conversion from DICOM, followed by thorough automatic and interactive quality control (QC), which is a must for a good DTI study. Our framework is centered around a DTI atlas that is either provided as a template or computed directly as an unbiased average atlas from the study data via deformable atlas building. Fiber tracts are defined via interactive tractography and clustering on that atlas. DTI fiber profiles are extracted automatically using the atlas mapping information. These tract parameter profiles are then analyzed using our statistics toolbox (FADTTS). The statistical results are then mapped back on to the fiber bundles and visualized with 3D Slicer. Results: This framework provides a coherent set of tools for DTI quality control and analysis. Conclusions: This framework will provide the field with a uniform process for DTI quality control and analysis.
KEYWORDS: Brain, Magnetic resonance imaging, Neuroimaging, Detection and tracking algorithms, Diffusion weighted imaging, Diffusion, Signal to noise ratio, Image resolution, Pathology, Data acquisition
Fiber tracking provides insights into the brain white matter network and has become more and more popular
in diffusion magnetic resonance (MR) imaging. Hardware or software phantom provides an essential platform
to investigate, validate and compare various tractography algorithms towards a "gold standard". Software
phantoms excel due to their flexibility in varying imaging parameters, such as tissue composition, SNR, as well
as potential to model various anatomies and pathologies. This paper describes a novel method in generating
diffusion MR images with various imaging parameters from realistically appearing, individually varying brain
anatomy based on predefined fiber tracts within a high-resolution human brain atlas. Specifically, joint, high
resolution DWI and structural MRI brain atlases were constructed with images acquired from 6 healthy subjects
(age 22-26) for the DWI data and 56 healthy subject (age 18-59) for the structural MRI data. Full brain fiber
tracking was performed with filtered, two-tensor tractography in atlas space. A deformation field based principal
component model from the structural MRI as well as unbiased atlas building was then employed to generate
synthetic structural brain MR images that are individually varying. Atlas fiber tracts were accordingly warped
into each synthetic brain anatomy. Diffusion MR images were finally computed from these warped tracts via a
composite hindered and restricted model of diffusion with various imaging parameters for gradient directions,
image resolution and SNR. Furthermore, an open-source program was developed to evaluate the fiber tracking
results both qualitatively and quantitatively based on various similarity measures.
In this work, we present a novel cortical correspondence method with application to the macaque brain. The correspondence method is based on sulcal curve constraints on a spherical deformable registration using spherical harmonics to parameterize the spherical deformation. Starting from structural MR images, we first apply existing preprocessing steps: brain tissue segmentation using the Automatic Brain Classification tool (ABC), as well as cortical surface reconstruction and spherical parametrization of the cortical surface via Constrained Laplacian-based Automated Segmentation with Proximities (CLASP). Then, initial correspondence between two cortical surfaces is automatically determined by a curve labeling method using sulcal landmarks extracted along sulcal fundic regions. Since the initial correspondence is limited to sulcal regions, we use spherical harmonics to extrapolate and regularize this correspondence to the entire cortical surface. To further improve the correspondence, we compute a spherical registration that optimizes the spherical harmonic parameterized deformation using a metric that incorporates the error over the sulcal landmarks as well as the normalized cross correlation of sulcal depth maps over the whole cortical surface. For evaluation, a normal 18-months-old macaque brain (for both left and right hemispheres) was matched to a prior macaque brain template with 9 manually labeled, major sulcal curves. The results show successful registration using the proposed registration approach. Evaluation results for optimal parameter settings are presented as well.
The use of regional connectivity measurements derived from diffusion imaging datasets has become of considerable interest in the neuroimaging community in order to better understand cortical and subcortical white matter connectivity. Current connectivity assessment methods are based on streamline fiber tractography, usually applied in a Monte-Carlo fashion. In this work we present a novel, graph-based method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. The computation is based on a multi-directional graph propagation method applied to sampled orientation distribution function (ODF), which can be computed directly from the original diffusion imaging data. We show early results of our method on synthetic and real datasets. The results illustrate the potential of our method towards subjectspecific connectivity measurements that are performed in an efficient, stable and reproducible manner. Such individual connectivity measurements would be well suited for application in population studies of neuropathology, such as Autism, Huntington's Disease, Multiple Sclerosis or leukodystrophies. The proposed method is generic and could easily be applied to non-diffusion data as long as local directional data can be derived.
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