3,501 research outputs found
Estimation of Fiber Orientations Using Neighborhood Information
Data from diffusion magnetic resonance imaging (dMRI) can be used to
reconstruct fiber tracts, for example, in muscle and white matter. Estimation
of fiber orientations (FOs) is a crucial step in the reconstruction process and
these estimates can be corrupted by noise. In this paper, a new method called
Fiber Orientation Reconstruction using Neighborhood Information (FORNI) is
described and shown to reduce the effects of noise and improve FO estimation
performance by incorporating spatial consistency. FORNI uses a fixed tensor
basis to model the diffusion weighted signals, which has the advantage of
providing an explicit relationship between the basis vectors and the FOs. FO
spatial coherence is encouraged using weighted l1-norm regularization terms,
which contain the interaction of directional information between neighbor
voxels. Data fidelity is encouraged using a squared error between the observed
and reconstructed diffusion weighted signals. After appropriate weighting of
these competing objectives, the resulting objective function is minimized using
a block coordinate descent algorithm, and a straightforward parallelization
strategy is used to speed up processing. Experiments were performed on a
digital crossing phantom, ex vivo tongue dMRI data, and in vivo brain dMRI data
for both qualitative and quantitative evaluation. The results demonstrate that
FORNI improves the quality of FO estimation over other state of the art
algorithms.Comment: Journal paper accepted in Medical Image Analysis. 35 pages and 16
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A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology
The human thalamus is a brain structure that comprises numerous, highly
specific nuclei. Since these nuclei are known to have different functions and
to be connected to different areas of the cerebral cortex, it is of great
interest for the neuroimaging community to study their volume, shape and
connectivity in vivo with MRI. In this study, we present a probabilistic atlas
of the thalamic nuclei built using ex vivo brain MRI scans and histological
data, as well as the application of the atlas to in vivo MRI segmentation. The
atlas was built using manual delineation of 26 thalamic nuclei on the serial
histology of 12 whole thalami from six autopsy samples, combined with manual
segmentations of the whole thalamus and surrounding structures (caudate,
putamen, hippocampus, etc.) made on in vivo brain MR data from 39 subjects. The
3D structure of the histological data and corresponding manual segmentations
was recovered using the ex vivo MRI as reference frame, and stacks of blockface
photographs acquired during the sectioning as intermediate target. The atlas,
which was encoded as an adaptive tetrahedral mesh, shows a good agreement with
with previous histological studies of the thalamus in terms of volumes of
representative nuclei. When applied to segmentation of in vivo scans using
Bayesian inference, the atlas shows excellent test-retest reliability,
robustness to changes in input MRI contrast, and ability to detect differential
thalamic effects in subjects with Alzheimer's disease. The probabilistic atlas
and companion segmentation tool are publicly available as part of the
neuroimaging package FreeSurfer
A Review on MR Image Intensity Inhomogeneity Correction
Intensity inhomogeneity (IIH) is often encountered in MR imaging,
and a number of techniques have been devised to correct this
artifact. This paper attempts to review some of the recent
developments in the mathematical modeling of IIH field.
Low-frequency models are widely used, but they tend to corrupt the
low-frequency components of the tissue. Hypersurface models and
statistical models can be adaptive to the image and generally more
stable, but they are also generally more complex and consume more
computer memory and CPU time. They are often formulated together
with image segmentation within one framework and the overall
performance is highly dependent on the segmentation process.
Beside these three popular models, this paper also summarizes
other techniques based on different principles. In addition, the
issue of quantitative evaluation and comparative study are
discussed
A goal-driven unsupervised image segmentation method combining graph-based processing and Markov random fields
Image segmentation is the process of partitioning a digital image into a set of homogeneous regions (according to some homogeneity criterion) to facilitate a subsequent higher-level analysis. In this context,
the present paper proposes an unsupervised and graph-based method of image segmentation, which is
driven by an application goal, namely, the generation of image segments associated with a user-defined
and application-specific goal. A graph, together with a random grid of source elements, is defined on
top of the input image. From each source satisfying a goal-driven predicate, called seed, a propagation
algorithm assigns a cost to each pixel on the basis of similarity and topological connectivity, measuring
the degree of association with the reference seed. Then, the set of most significant regions is automatically extracted and used to estimate a statistical model for each region. Finally, the segmentation problem is expressed in a Bayesian framework in terms of probabilistic Markov random field (MRF) graphical
modeling. An ad hoc energy function is defined based on parametric models, a seed-specific spatial feature, a background-specific potential, and local-contextual information. This energy function is minimized
through graph cuts and, more specifically, the alpha-beta swap algorithm, yielding the final goal-driven
segmentation based on the maximum a posteriori (MAP) decision rule. The proposed method does not
require deep a priori knowledge (e.g., labelled datasets), as it only requires the choice of a goal-driven
predicate and a suited parametric model for the data. In the experimental validation with both magnetic
resonance (MR) and synthetic aperture radar (SAR) images, the method demonstrates robustness, versatility, and applicability to different domains, thus allowing for further analyses guided by the generated
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