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.
Simulated data is an important tool for evaluation of reconstruction and image processing algorithms in the frequent absence of ground truth, in-vivo data from living subjects. This is especially true in the case of dynamic PET studies, in which counting statistics of the volume can vary widely over the time-course of the acquisition. Realistic simulated data-sets which model anatomy and physiology, and make explicit the spatial and temporal image acquisition characteristics, facilitate experimentation with a wide range of the conditions anticipated in practice, and which can severely challenge algorithm performance and reliability. As a first example, we have developed a realistic dynamic FDG-PET data-set using the PET-SORTEO Monte Carlo simulation code and the MNI digital brain phantom. The phantom is a three-dimensional data-set that defines the spatial distribution of different tissues. Time activity curves were calculated using an impulse response function specified by generally accepted rate constants, convolved with an input function obtained by blood sampling, and assigned to grey and white matter tissue regions. We created a dynamic PET study using PET-SORTEO configured to simulate an ECAT Exact HR+. The resulting sinograms were reconstructed with all corrections, using variations of FBP and OSEM. Having constructed the dynamic PET data-sets, we used them to evaluate the performance of intensity-based registration as part of a tool for quantifying hyper/hypo perfusion with particular application to analysis of brain dementia scans, and a study of the stability of kinetic parameter estimation.
KEYWORDS: Magnetic resonance imaging, 3D modeling, Spine, Data modeling, Arteries, Blood vessels, 3D image processing, Process modeling, Visualization, Tissues
A vessel extraction approach is presented that permits visualization of the cerebral vasculature in 3D from anatomical proton density (PD) weighted magnetic resonance imaging (MRI) volumes. The approach presented utilizes general knowledge about the shape and size of the cerebral vasculature and is divided into multi-scale vessel enhancement filtering, centre-line extraction, and surface modeling. To improve the discrimination between blood vessels and other tissue a multi-scale filtering method that enhances tubular structures is used as a pre-processing step. Centre-line extraction is applied to roughly estimate the centre-line of the vasculature involving both segmentation and skeletonization. The centre-line is used to initialize an active contour modeling process where cylinders are used to model the 3D surface of the blood vessels. The accuracy and robustness of the vessel extraction approach have been demonstrated on both simulated and real data (1mm3 voxels). On simulated data, the mean error of the estimated radii was found to be less than 0.4mm. On real data, the vasculature was successfully extracted from 20 MRI data sets using the same input parameters. An expert found the extracted vessel surfaces to coincide with the vessel walls in the data. Results from CTA data indicate that the approach will work successfully with other imaging modalities as well.
KEYWORDS: Magnetic resonance imaging, Visualization, Brain, 3D vision, Animal model studies, 3D acquisition, Data modeling, 3D modeling, Basal ganglia, Cryogenics
Many of the critical basal ganglia structures are not distinguishable on anatomical magnetic resonance imaging (MRI) scans, even though they differ in functionality. In order to provide the neurosurgeon with this missing information, a deformable volumetric atlas of the basal ganglia has been created from the Shaltenbrand and Wahren atlas of cryogenic slices. The volumetric atlas can be non-linearly deformed to an individual patient's MRI. To facilitate the clinical use of the atlas, a visualization platform has been developed for pre- and intra-operative use which permits manipulation of the merged atlas and MRI data sets in two- and three-dimensional views. The platform includes graphical tools which allow the visualization of projections of the leukotome and other surgical tools with respect to the atlas data, as well as pre- registered images from any other imaging modality. In addition, a graphical interface has been designed to create custom virtual lesions using computer models of neurosurgical tools for intra-operative planning. To date 17 clinical cases have been successfully performed using the described system.
KEYWORDS: Brain, Data modeling, Magnetic resonance imaging, Brain mapping, 3D modeling, Image registration, 3D acquisition, 3D image processing, Detection and tracking algorithms, Data acquisition
We describe an automated method to register MRI volumetric datasets to a digital human brain model. The technique employs 3D non-linear warping based on the estimation of local deformation fields using cross-correlation of invariant intensity features derived from image data. Results of the non-linear registration on a simple phantom, a complex brain phantom and real MRI data are presented. Anatomical variability is expressed with respect to the Talairach-like standardized brain-based coordinate system of the model. We show that the automated non-linear registration reduces the inter-subject variability of homologous points in standardized space by 15% over linear registration methods. A 3D variability map is shown.
KEYWORDS: Data modeling, 3D modeling, Magnetic resonance imaging, Image segmentation, Brain, Skull, Magnetism, Brain mapping, Neuroimaging, 3D image processing
An iterative algorithm is presented for simultaneous deformation of multiple curves and surfaces to an MRI, with inter-surface constraints and self-intersection avoidance. The resulting robust segmentation, combined with local curvature matching, automatically creates surfaces of MRI datasets with a common mapping to surface parametric space.
KEYWORDS: Data modeling, Image segmentation, Brain, 3D modeling, Magnetic resonance imaging, Neuroimaging, 3D image processing, Visualization, Biomedical optics, Image registration
This paper proposes a methodology that enables an arbitrary 3-D MRI brain image-volume to be automatically segmented and classified into neuro-anatomical components using multiresolution registration and matching with a novel volumetric brain structure model (VBSM). This model contains both raster and geometric data. The raster component comprises the mean MRI volume after a set of individual volumes of normal volunteers have been transformed to a standardized brain-based coordinate space. The geometric data consists of polyhedral objects representing anatomically important structures such as cortical gyri and deep gray matter nuclei. The method consists of iteratively registering the data set to be segmented to the VBSM using deformations based on local image correlation. This segmentation process is performed hierarchically in scale-space. Each step in decreasing levels of scale refines the fit of the previous step and provides input to the next. Results from phantom and real MR data are presented.
KEYWORDS: Image segmentation, Brain, Data modeling, Tissues, 3D modeling, Neuroimaging, Magnetic resonance imaging, Visualization, Biomedical optics, Visual process modeling
This paper describes the development and use of a brain tissue probability model for the segmentation of multiple sclerosis lesions in magnetic resonance (MR) images of the human brain. Based on MR data obtained from a group of healthy volunteers, the model was constructed to provide prior probabilities of grey matter, white matter, ventricular cerebrospinal fluid (CSF), and external CSF distribution per unit voxel in a standardized 3- dimensional `brain space.' In comparison to purely data-driven segmentation, the use of the model to guide the segmentation of multiple sclerosis lesions reduced the volume of false positive lesions by 50%.
We describe the implementation, experience and preliminary results obtained with a 3-D computerized brain atlas for topographical and functional analysis of brain sub-regions. A volume-of-interest (VOI) atlas was produced by manual contouring on 64 adjacent 2 mm-thick MRI slices to yield 60 brain structures in each hemisphere which could be adjusted, originally by global affine transformation or local interactive adjustments, to match individual MRI datasets. We have now added a non-linear deformation (warp) capability (Bookstein, 1989) into the procedure for fitting the atlas to the brain data. Specific target points are identified in both atlas and MRI spaces which define a continuous 3-D warp transformation that maps the atlas on to the individual brain image. The procedure was used to fit MRI brain image volumes from 16 young normal volunteers. Regional volume and positional variability were determined, the latter in such a way as to assess the extent to which previous linear models of brain anatomical variability fail to account for the true variation among normal individuals. Using a linear model for atlas deformation yielded 3-D fits of the MRI data which, when pooled across subjects and brain regions, left a residual mis-match of 6 - 7 mm as compared to the non-linear model. The results indicate a substantial component of morphometric variability is not accounted for by linear scaling. This has profound implications for applications which employ stereotactic coordinate systems which map individual brains into a common reference frame: quantitative neuroradiology, stereotactic neurosurgery and cognitive mapping of normal brain function with PET. In the latter case, the combination of a non-linear deformation algorithm would allow for accurate measurement of individual anatomic variations and the inclusion of such variations in inter-subject averaging methodologies used for cognitive mapping with PET.
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