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We describe a new fully automatic method for the segmentation of brain images that contain multiple sclerosis white matter lesions. Multichannel magnetic resonance images are used to delineate multiple sclerosis lesions while segmenting the brain into its major structures. The method is an atlas-based segmentation technique employing a topological atlas as(More)
The subthalamic nucleus (STN) is a small but vitally important structure in the basal ganglia. Because of its small volume, and its localization in the basal ganglia, the STN can best be visualized using ultra-high resolution 7 Tesla (T) magnetic resonance imaging (MRI). In the present study, first we individually segmented 7 T MRI STN masks to generate(More)
Improvements in the spatial resolution of structural and functional MRI are beginning to enable analysis of intracortical structures such as heavily myelinated layers in 3D, a prerequisite for in-vivo parcellation of individual human brains. This parcellation can only be performed precisely if the profiles used in cortical analysis are anatomically(More)
Diffusion tensor imaging (DTI) is widely used to characterize tissue micro-architecture and brain connectivity. In regions of crossing fibers, however, the tensor model fails because it cannot represent multiple, independent intra-voxel orientations. Most of the methods that have been proposed to resolve this problem require diffusion magnetic resonance(More)
The subthalamic nucleus (STh) is a small subcortical structure which is involved in regulating motor as well as cognitive functions. Due to its small size and close proximity to other small subcortical structures, it has been a challenge to localize and visualize it using magnetic resonance imaging (MRI). Currently there are several standard atlases(More)
This paper presents a computational framework for whole brain segmentation of 7Tesla magnetic resonance images able to handle ultra-high resolution data. The approach combines multi-object topology-preserving deformable models with shape and intensity atlases to encode prior anatomical knowledge in a computationally efficient algorithm. Experimental(More)
Diffusion tensor imaging (DTI) is widely used to characterize white matter in health and disease. Previous approaches to the estimation of diffusion tensors have either been statistically suboptimal or have used Gaussian approximations of the underlying noise structure, which is Rician in reality. This can cause quantities derived from these tensors - e.g.,(More)
Segmentation of brain images often requires a statistical atlas for providing prior information about the spatial position of different structures. A major limitation of atlas-based segmentation algorithms is their deficiency in analyzing brains that have a large deviation from the population used in the construction of the atlas. We present an(More)
Consideration of spatially variable noise fields is becoming increasingly necessary in MRI given recent innovations in artifact identification and statistically driven image processing. Fast imaging methods enable study of difficult anatomical targets and improve image quality but also increase the spatial variability in the noise field. Traditional(More)
Structural brain data is key for the understanding of brain function and networks, i.e., connectomics. Here we present data sets available from the 'atlasing of the basal ganglia (ATAG)' project, which provides ultra-high resolution 7 Tesla (T) magnetic resonance imaging (MRI) scans from young, middle-aged, and elderly participants. The ATAG data set(More)