IEEE transactions on medical imaging
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IEEE Trans Med Imaging · Nov 2007
Impact of an improved combination of signals from array coils in diffusion tensor imaging.
An improved method for the combination of signals from array coils is presented as a way to reduce the influence of the noise floor on the estimation of diffusion tensor imaging (DTI) parameters. By an optimized combination of signals from the array channels and complex averaging of measurements, this method leads to a significant reduction of the noise bias. ⋯ In many applications, the use of this combination method would result in a scan time reduction in comparison to the current standard. The effects of the improved combination on diffusion decay curves, fractional anisotropy maps, and apparent diffusion coefficient (ADC) profiles are demonstrated.
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IEEE Trans Med Imaging · Nov 2007
High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: an example study using amyotrophic lateral sclerosis.
Spatial normalization of diffusion tensor images plays a key role in voxel-based analysis of white matter (WM) group differences. Currently, it has been achieved using low-dimensional registration methods in the large majority of clinical studies. This paper aims to motivate the use of high-dimensional normalization approaches by generating evidence of their impact on the findings of such studies. ⋯ Each method was assessed in terms of its ability to detect significant differences between ALS patients and controls. Our findings suggest that inadequate normalization with low-dimensional approaches can result in insufficient removal of shape differences which in turn can confound FA differences in a complex manner, and that utilizing high-dimensional normalization can both significantly minimize the confounding effect of shape differences to FA differences and provide a more complete description of WM differences in terms of both size and tissue architecture differences. We also found that high-dimensional approaches, by leveraging full tensor features instead of tensor-derived indices, can further improve the alignment of WM tracts.
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IEEE Trans Med Imaging · Nov 2007
Representing diffusion MRI in 5-D simplifies regularization and segmentation of white matter tracts.
We present a new five-dimensional (5-D) space representation of diffusion magnetic resonance imaging (dMRI) of high angular resolution. This 5-D space is basically a non-Euclidean space of position and orientation in which crossing fiber tracts can be clearly disentangled, that cannot be separated in three-dimensional position space. This new representation provides many possibilities for processing and analysis since classical methods for scalar images can be extended to higher dimensions even if the spaces are not Euclidean. ⋯ The purpose of this paper is to explore the possibility of segmenting white matter structures directly as entirely separated bundles in this 5-D space. We will present results from a synthetic model and results on real data of a human brain acquired with diffusion spectrum magnetic resonance imaging (MRI), one of the dMRI of high angular resolution available. These results will lead us to the conclusion that this new high-dimensional representation indeed simplifies the problem of segmentation and regularization.
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IEEE Trans Med Imaging · Nov 2007
A probabilistic model-based approach to consistent white matter tract segmentation.
Since the invention of diffusion magnetic resonance imaging (dMRI), currently the only established method for studying white matter connectivity in a clinical environment, there has been a great deal of interest in the effects of various pathologies on the connectivity of the brain. As methods for in vivo tractography have been developed, it has become possible to track and segment specific white matter structures of interest for particular study. ⋯ In this work we take a different approach, developing a formal probabilistic model for the relationships between comparable tracts in different scans, and then using it to choose a tract, a posteriori, which best matches a predefined reference tract for the structure of interest. We demonstrate that this method is able to significantly improve segmentation consistency without directly constraining the tractography algorithm.
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IEEE Trans Med Imaging · Sep 2007
Symmetric data attachment terms for large deformation image registration.
Nonrigid medical image registration between images that are linked by an invertible transformation is an inherently symmetric problem. The transformation that registers the image pair should ideally be the inverse of the transformation that registers the pair with the order of images interchanged. This property is referred to as symmetry in registration or inverse consistent registration. ⋯ In this paper, we propose two novel cost functions in the large deformation diffeomorphic framework that are inverse consistent. These cost functions have symmetric data-attachment terms; in the first, the matching error is measured at all points along the flow between template and target, and in the second, matching is enforced only at the midpoint of the flow between the template and target. We have implemented these cost functions and present experimental results to validate their inverse consistent property and registration accuracy.