Faculty Bibliography

RATIONALE AND OBJECTIVES: Manual segmentation of brain tumors from magnetic resonance images is a challenging and time-consuming task. An automated system has been developed for brain tumor segmentation that will provide objective, reproducible segmentations that are close to the manual results. Additionally, the method segments white matter, grey matter, cerebrospinal fluid, and edema. The segmentation of pathology and healthy structures is crucial for surgical planning and intervention. MATERIALS AND METHODS: The method performs the segmentation of a registered set of magnetic resonance images using an expectation-maximization scheme. The segmentation is guided by a spatial probabilistic atlas that contains expert prior knowledge about brain structures. This atlas is modified with the subject-specific brain tumor prior that is computed based on contrast enhancement. RESULTS: Five cases with different types of tumors are selected for evaluation. The results obtained from the automatic segmentation program are compared with results from manual and semi-automated methods. The automated method yields results that have surface distances at roughly 1-4 mm compared with the manual results. CONCLUSION: The automated method can be applied to different types of tumors. Although its performance is below that of the semi-automated method, it has the advantage of requiring no user supervision.

PURPOSE: To evaluate the normal brains of adults and neonates for regional and age-related differences in apparent diffusion coefficient (ADC) and fractional anisotropy (FA). MATERIALS AND METHODS: Eight healthy adults and 20 healthy neonates were examined with a 3.0-T head-only magnetic resonance (MR) imaging unit by using a single-shot diffusion-tensor sequence. Trace ADC maps, FA maps, directional maps of the putative directions of white matter (WM) tracts, and fiber-tracking maps were obtained. Regions of interest-eight in WM and one in gray matter (GM)-were predefined for the ADC and FA measurements. The Student t test was used to compare FA and ADC between adults and neonates, whereas the Tukey multiple-comparison test was used to compare FA and ADC in different brain regions in the adult and neonate groups. RESULTS: A global elevation in ADC (P <.001) in both GM and WM and a reduction in FA (P <.001) in WM were observed in neonates as compared with these values in adults. In addition, significant regional variations in FA and ADC were observed in both groups. Regional variations in FA and ADC were less remarkable in adults, whereas neonates had consistently higher FA values and lower ADC values in the central WM as compared with these values in the peripheral WM. Fiber tracking revealed only major WM tracts in the neonates but fibers extending to the peripheral WM in the adults. CONCLUSION: There were regional differences in FA and ADC values in the neonates; such variations were less remarkable in the adults.

This paper presents a novel processing scheme for the automatic and robust computation of a medial shape model, which represents an object population with shape variability. The sensitivity of medial descriptions to object variations and small boundary perturbations are fundamental problems of any skeletonization technique. These problems are approached with the computation of a model with common medial branching topology and grid sampling. This model is then used for a medial shape description of individual objects via a constrained model fit. The process starts from parametric 3D boundary representations with existing point-to-point homology between objects. The Voronoi skeleton of each sampled object boundary is partitioned into non-branching medial sheets and simplified by a novel pruning algorithm using a volumetric contribution criterion. Using the surface homology, medial sheets are combined to form a common medial branching topology. Finally, the medial sheets are sampled and represented as meshes of medial primitives. Results on populations of up to 184 biological objects clearly demonstrate that the common medial branching topology can be described by a small number of medial sheets and that even a coarse sampling leads to a close approximation of individual objects.

In the past 10 to 15 years, 1.5T has been one of the most commonly used field strengths for day-to-day clinical operations. However, recent advances in high field technology and the increased availability of high field (> 1.5T) human scanners have opened the doors for a variety of exciting improvements in clinical and research applications of MR imaging. In particular, 3T has continued to gain wide acceptance as one of the main field strengths for clinical and research studies. Therefore, in this article the authors focus on the pros and cons of 3T imaging and comparisons between results obtained at 3T and 1.5T.

Medial representation of a three-dimensional (3-D) object or an ensemble of 3-D objects involves capturing the object interior as a locus of medial atoms, each atom being two vectors of equal length,joined at the tail, at the medial point. Medial representation has a variety of beneficial properties, among the most important of which are 1) its inherent,geometry, provides an object-intrinsic coordinate system and thus provides correspondence between instances of the object in and near the object(s); 2) it captures the object interior and is, thus, very suitable for deformation; and 3) it provides the basis for an intuitive object-based multiscale sequence leading to efficiency of segmentation algorithms and trainability of statistical characterizations with limited training sets. As a result of these properties, medial representation is particularly suitable for the following image analysis tasks; how each operates will be described and will be illustrated by results:. 1) segmentation of objects and object complexes via deformable models;. 2) segmentation of tubular trees, e.g., of blood vessels, by following height ridges of measures of fit of medial atoms to target images;. 3) object-based image registration via medial loci of such blood vessel trees;. 4) statistical characterization of shape differences between control and pathological classes of structures. These analysis tasks are made possible by a new form of medial representation called m-reps, which is described.

The clinical recognition of abnormal vascular tortuosity, or excessive bending, twisting, and winding, is important to the diagnosis of many diseases. Automated detection and quantitation of abnormal vascular tortuosity from three-dimensional (3-D) medical image data would, therefore, be of value. However, previous research has centered primarily upon two-dimensional (2-D) analysis of the special subset of vessels whose paths are normally close to straight. This report provides the first 3-D tortuosity analysis of clusters of vessels within the normally tortuous intracerebral circulation. We define three different clinical patterns of abnormal tortuosity. We extend into 3-D two tortuosity metrics previously reported as useful in analyzing 2-D images and describe a new metric that incorporates counts of minima of total curvature. We extract vessels from MRA data, map corresponding anatomical regions between sets of normal patients and patients with known pathology, and evaluate the three tortuosity metrics for ability to detect each type of abnormality within the region of interest. We conclude that the new tortuosity metric appears to be the most effective in detecting several types of abnormalities. However, one of the other metrics, based on a sum of curvature magnitudes, may be more effective in recognizing tightly coiled, "corkscrew" vessels associated with malignant tumors.

Knowledge about the biological variability of anatomical objects is essential for statistical shape analysis and discrimination between healthy and pathological structures. This paper describes a novel approach that incorporates the variability of an object population into the generation of a characteristic 3D shape model. The proposed shape representation is a coarse-scale sampled medial description derived from a fine-scale spherical harmonics (SPHARM) boundary description. This medial description is composed of a net of medial samples (m-rep) with fixed graph properties. The medial model is computed automatically from a predefined shape space using pruned 3D Voronoi skeletons. A new method determines the stable medial branching topology from the shape space. An intrinsic coordinate system and an implicit correspondence between shapes is defined on the medial manifold. Several studies of biological structures clearly demonstrate that the novel representation has the promise to describe shape changes in a natural and intuitive way. A new medial shape similarity study of group differences between monozygotic and dizygotic twins in lateral ventricle shape demonstrates the meaningful and powerful representation of local and global form.

This paper presents a general framework for analyzing structural and radiometric asymmetry in brain images. In a healthy brain, the left and right hemispheres are largely symmetric across the mid-sagittal plane. Brain tumors may belong to one or both of the following categories: mass-effect, in which the diseased tissue displaces healthy tissue; and infiltrating, in which healthy tissue has become diseased. Mass-effect brain tumors cause structural asymmetry by displacing healthy tissue, and may cause radiometric asymmetry in adjacent normal structures due to edema. Infiltrating tumors have a different radiometric response from healthy tissue. Thus, structural and radiometric asymmetries across the mid-sagittal plane in brain images provide important cues that tumors may be present. We have developed a framework that registers images with their reflections across the mid-sagittal plane. The registration process accounts for tissue displacement through large deformation image warping. Radiometric differences are taken into account through an additive intensity field. We present an efficient multi-scale algorithm for the joint estimation of structural and radiometric asymmetry. Results for nine MR images of patients with tumors and four normal control subjects are presented.