One of the most common approaches to MRI brain tissue segmentation is to employ an atlas prior to initialize an Expectation- Maximization (EM) image labeling scheme using a statistical model of MRI intensities. This prior is commonly derived from a set of manually segmented training data from the population of interest. However, in cases where subject anatomy varies significantly from the prior anatomical average model (for example in the case where extreme developmental abnormalities or brain injuries occur), the prior tissue map does not provide adequate information about the observed MRI intensities to ensure the EM algorithm converges to an anatomically accurate labeling of the MRI. In this paper, we present a novel approach for automatic segmentation of such cases. This approach augments the atlas-based EM segmentation by exploring methods to build a hybrid tissue segmentation scheme that seeks to learn where an atlas prior fails (due to inadequate representation of anatomical variation in the statistical atlas) and utilize an alternative prior derived from a patch driven search of the atlas data. We describe a framework for incorporating this patch-based augmentation of EM (PBAEM) into a 4D age-specific atlas-based segmentation of developing brain anatomy. The proposed approach was evaluated on a set of MRI brain scans of premature neonates with ages ranging from 27.29 to 46.43 gestational weeks (GWs). Results indicated superior performance compared to the conventional atlas-based segmentation method, providing improved segmentation accuracy for gray matter, white matter, ventricles and sulcal CSF regions.
KEYWORDS: Motion estimation, Magnetic resonance imaging, 3D image processing, 3D image reconstruction, 3D acquisition, Head, 3D modeling, Reconstruction algorithms, Medical imaging, Fetus
We describe a free software tool which combines a set of algorithms that provide a framework for building 3D
volumetric images of regions of moving anatomy using multiple fast multi-slice MRI studies. It is specifically
motivated by the clinical application of unsedated fetal brain imaging, which has emerged as an important area
for image analysis. The tool reads multiple DICOM image stacks acquired in any angulation into a consistent
patient coordinate frame and allows the user to select regions to be locally motion corrected. It combines
algorithms for slice motion estimation, bias field inconsistency correction and 3D volume reconstruction from
multiple scattered slice stacks. The tool is built onto the RView (http://rview.colin-studholme.net) medical
image display software and allows the user to inspect slice stacks, and apply both stack and slice level motion
estimation that incorporates temporal constraints based on slice timing and interleave information read from
the DICOM data. Following motion estimation an algorithm for bias field inconsistency correction provides the
user with the ability to remove artifacts arising from the motion of the local anatomy relative to the imaging
coils. Full 3D visualization of the slice stacks and individual slice orientations is provided to assist in evaluating
the quality of the motion correction and final image reconstruction. The tool has been evaluated on a range of
clinical data acquired on GE, Siemens and Philips MRI scanners.
Understanding human brain development in utero and detecting cortical abnormalities related to specific clinical
conditions is an important area of research. In this paper, we describe and evaluate methodology for detection and
mapping of delays in early cortical folding from population-based studies of fetal brain anatomies imaged in utero.
We use a general linear modeling framework to describe spatiotemporal changes in curvature of the developing
brain and explore the ability to detect and localize delays in cortical folding in the presence of uncertainty
in estimation of the fetal age. We apply permutation testing to examine which regions of the brain surface
provide the most statistical power to detect a given folding delay at a given developmental stage. The presented
methodology is evaluated using MR scans of fetuses with normal brain development and gestational ages ranging
from 20.57 to 27.86 weeks. This period is critical in early cortical folding and the formation of the primary and
secondary sulci. Finally, we demonstrate a clinical application of the framework for detection and localization of
folding delays in fetuses with isolated mild ventriculomegaly.
Recent studies reported the development of methods for rigid registration of 2D fetal brain imaging data to
correct for unconstrained fetal and maternal motion, and allow the formation of a true 3D image of conventional
fetal brain anatomy from conventional MRI. Diffusion tensor imaging provides additional valuable insight into
the developing brain anatomy, however the correction of motion artifacts in clinical fetal diffusion imaging is
still a challenging problem. This is due to the challenging problem of matching lower signal-to-noise ratio
diffusion weighted EPI slice data to recover between-slice motion, compounded by the presence of possible
geometric distortions in the EPI data. In addition, the problem of estimating a diffusion model (such as a
tensor) on a regular grid that takes into account the inconsistent spatial and orientation sampling of the diffusion
measurements needs to be solved in a robust way. Previous methods have used slice to volume registration within
the diffusion dataset. In this work, we describe an alternative approach that makes use of an alignment of diffusion
weighted EPI slices to a conventional structural MRI scan which provides a geometrically correct reference image.
After spatial realignment of each diffusion slice, a tensor field representing the diffusion profile is estimated by
weighted least squared fitting. By qualitative and quantitative evaluation of the results, we confirm the proposed
algorithm successfully corrects the motion and reconstructs the diffusion tensor field.
Recent advances in MR and image analysis allow for reconstruction of high-resolution 3D images from clinical
in utero scans of the human fetal brain. Automated segmentation of tissue types from MR images (MRI) is
a key step in the quantitative analysis of brain development. Conventional atlas-based methods for adult brain
segmentation are limited in their ability to accurately delineate complex structures of developing tissues from
fetal MRI. In this paper, we formulate a novel geometric representation of the fetal brain aimed at capturing the
laminar structure of developing anatomy. The proposed model uses a depth-based encoding of tissue occurrence
within the fetal brain and provides an additional anatomical constraint in a form of a laminar prior that can
be incorporated into conventional atlas-based EM segmentation. Validation experiments are performed using
clinical in utero scans of 5 fetal subjects at gestational ages ranging from 20.5 to 22.5 weeks. Experimental
results are evaluated against reference manual segmentations and quantified in terms of Dice similarity coefficient
(DSC). The study demonstrates that the use of laminar depth-encoded tissue priors improves both the overall
accuracy and precision of fetal brain segmentation. Particular refinement is observed in regions of the parietal
and occipital lobes where the DSC index is improved from 0.81 to 0.82 for cortical grey matter, from 0.71 to
0.73 for the germinal matrix, and from 0.81 to 0.87 for white matter.
KEYWORDS: Brain, Interfaces, Magnetic resonance imaging, In vivo imaging, Image segmentation, Convolution, Image processing, Natural surfaces, Data acquisition, Time metrology
In this paper we describe the application of folding measures to tracking in vivo cortical brain development in premature neonatal brain anatomy. The outer gray matter and the gray-white matter interface surfaces were extracted from semi-interactively segmented high-resolution T1 MRI data. Nine curvature- and geometric descriptor-based folding measures were applied to six premature infants, aged 28-37 weeks, using a direct voxelwise iso-surface representation. We have shown that using such an approach it is feasible to extract meaningful surfaces of adequate quality from typical clinically acquired neonatal MRI data. We have shown that most of the folding measures, including a new proposed measure, are sensitive to changes in age and therefore applicable in developing a model that tracks development in premature infants. For the first time gyrification measures have been computed on the gray-white matter interface and on cases whose age is representative of a period of intense brain development.
The implementation of Magnetic Resonance Spectroscopic Imaging (MRSI) for diagnostic imaging benefits from close integration of
the lower-spatial resolution MRSI information with information
from high-resolution structural MRI. Since patients can commonly
move between acquisitions, it is necessary to account for possible
mis-registration between the datasets arising from differences in
patient positioning. In this paper we evaluate the use of 4 common
multi-modality registration criteria to recover alignment between
high resolution structural MRI and 3D MRSI data of the brain with
sub-voxel accuracy. We explore the use of alternative MRSI water
reference images to provide different types of structural information for the alignment process. The alignment accuracy was
evaluated using both synthetically created MRSI and MRI data and a
set of carefully collected subject image data with known ground
truth spatial transformation between image volumes. The final
accuracy and precision of estimates were assessed using multiple
random starts of the registration algorithm. Sub voxel accuracy
was found by all four similarity criteria with normalized mutual
information providing the lowest target registration error for the
7 subject images. This effort supports the ongoing development of
a database of brain metabolite distributions in normal subjects,
which will be used in the evaluation of metabolic changes in
neurological diseases.
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