Faculty Bibliography

One goal of statistical shape analysis is the discrimination between two populations of objects. Whereas traditional shape analysis was mostly concerned with single objects, analysis of multi-object complexes presents new challenges related to alignment and pose. In this paper, we present a methodology for discriminant analysis of multiple objects represented by sampled medial manifolds. Non-euclidean metrics that describe geodesic distances between sets of sampled representations are used for alignment and discrimination. Our choice of discriminant method is the distance-weighted discriminant because of its generalization ability in high-dimensional, low sample size settings. Using an unbiased, soft discrimination score, we associate a statistical hypothesis test with the discrimination results. We explore the effectiveness of different choices of features as input to the discriminant analysis, using measures like volume, pose, shape, and the combination of pose and shape. Our method is applied to a longitudinal pediatric autism study with 10 subcortical brain structures in a population of 70 subjects. It is shown that the choices of type of global alignment and of intrinsic versus extrinsic shape features, the latter being sensitive to relative pose, are crucial factors for group discrimination and also for explaining the nature of shape change in terms of the application domain.

Understanding myelination in early brain development is of clinical importance, as many neurological disorders have their origin in early cerebral organization and maturation. The goal of this work is to study a large neonate database acquired with standard MR imagery to illuminate effects of early development in MRI. 90 neonates were selected from a study of healthy brain development. Subjects were imaged via MRI postnatally. MR acquisition included high-resolution structural and diffusion tensor images. Unbiased atlases for structural and DTI data were generated and co-registered into a single coordinate frame for voxel-wise comparison of MR and DTI appearance across time. All original datasets were mapped into this frame and structural image data was additionally intensity normalized. In addition, myelinated white matter probabilistic segmentations from our neonate tissue segmentation were mapped into the same space to study how our segmentation results were affected by the changing intensity characteristics in early development Linear regression maps and p-value maps were computed and visualized. The resulting visualization of voxels-wise corresponding maps of all MR and DTI properties captures early development information in MR imagery. Surprisingly, we encountered regions of seemingly decreased myelinated WM probability over time even though we expected a confident increase for all of the brain. The intensity changes in the MR images in those regions help explain this counterintuitive result. The regressional results indicate that this is an effect of intensity changes due not solely to myelination processes but also likely brain dehydration processes in early postnatal development.

Compared to region of interest based DTI analysis, voxel-based analysis gives higher degree of localization and avoids the procedure of manual delineation with the resulting intra and inter-rater variability. One of the major challenges in voxel-wise DTI analysis is to get high quality voxel-level correspondence. For that purpose, current DTI analysis tools are building on nonlinear registration algorithms that deform individual datasets into a template image that is either precomputed or computed as part of the analysis. A variety of matching criteria and deformation schemes have been proposed, but often comparative evaluation is missing. In our opinion, the use of consistent and unbiased measures to evaluate current DTI procedures is of great importance and our work presents two possible measures. Specifically, we propose the evaluation criteria generalization and specificity, originally introduced by the shape modeling community, to evaluate and compare different DTI nonlinear warping results. These measures are of indirect nature and have a population wise view. Both measures incorporate information of the variability of the registration results in the template space via a voxel-wise PCA model. Thus far, we have used these measures to evaluate our own DTI analysis procedure employing fluid-based registration on scalar DTI maps. Generalization and specificity from tensor images in the template space were computed for 8 scalar property maps. We found that for our procedure an intensity-normalized FA feature outperformed the other scalar measurements. Also, using the tensor images rather than the FA maps as a comparison frame seemed to produce more robust results.

The human brain undergoes significant changes in the first few years after birth, but knowledge about this critical period of development is quite limited. Previous neuroimaging studies have been mostly focused on morphometric measures such as volume and shape, although tissue property measures related to the degree of myelination and axon density could also add valuable information to our understanding of brain maturation. Our goal is to complement brain growth analysis via morphometry with the study of longitudinal tissue property changes as reflected in patterns observed in multi-modal structural MRI and DTI. Our preliminary study includes eight healthy pediatric subjects with repeated scans at the age of two weeks, one year, and two years with T1, T2, PD, and DT MRI. Analysis is driven by the registration of multiple modalities and time points within and between subjects into a common coordinate frame, followed by image intensity normalization. Quantitative tractography with diffusion and structural image parameters serves for multi-variate tissue analysis. Different patterns of rapid changes were observed in the corpus callosum and the posterior and anterior internal capsule, structures known for distinctly different myelination growth. There are significant differences in central versus peripheral white matter, and also a wm/gm contrast flip in both T1 and T2 images but not diffusion parameters. We demonstrate that the combined longitudinal analysis of structural and diffusion MRI proves superior to individual modalities and might provide a better understanding of the trajectory of early neurodevelopment.

Diffusion Tensor Imaging (DTI) has become an important MRI procedure to investigate the integrity of white matter in brain in vivo. DTI is estimated from a series of acquired Diffusion Weighted Imaging (DWI) volumes. DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. Currently, routine DTI QC procedures are conducted manually by visually checking the DWI data set in a gradient by gradient and slice by slice way. The results often suffer from low consistence across different data sets, lack of agreement of different experts, and difficulty to judge motion artifacts by qualitative inspection. Additionally considerable manpower is needed for this step due to the large number of images to QC, which is common for group comparison and longitudinal studies, especially with increasing number of diffusion gradient directions. We present a framework for automatic DWI QC. We developed a tool called DTIPrep which pipelines the QC steps with a detailed protocoling and reporting facility. And it is fully open source. This framework/tool has been successfully applied to several DTI studies with several hundred DWIs in our lab as well as collaborating labs in Utah and Iowa. In our studies, the tool provides a crucial piece for robust DTI analysis in brain white matter study.

Analysis of human brain development is a crucial step for improved understanding of neurodevelopmental disorders. We focus on normal brain development as is observed in the multimodal longitudinal MRI/DTI data of neonates to two years of age. We present a spatio-temporal analysis framework using Gompertz function as a population growth model with three different spatial localization strategies: voxel-based, data driven clustering and atlas driven regional analysis. Growth models from multimodal imaging channels collected at each voxel form feature vectors which are clustered using the Dirichlet Process Mixture Models (DPMM). Clustering thus combines growth information from different modalities to subdivide the image into voxel groups with similar properties. The processing generates spatial maps that highlight the dynamic progression of white matter development. These maps show progression of white matter maturation where primarily, central regions mature earlier compared to the periphery, but where more subtle regional differences in growth can be observed. Atlas based analysis allows a quantitative analysis of a specific anatomical region, whereas data driven clustering identifies regions of similar growth patterns. The combination of these two allows us to investigate growth patterns within an anatomical region. Specifically, analysis of anterior and posterior limb of internal capsule show that there are different growth trajectories within these anatomies, and that it may be useful to divide certain anatomies into subregions with distinctive growth patterns.

Deformable image registration in the presence of considerable contrast differences and large-scale size and shape changes represents a significant challenge for image registration. A representative driving application is the study of early brain development in neuroimaging, which requires co-registration of images of the same subject across time or building 4-D population atlases. Growth during the first few years of development involves significant changes in size and shape of anatomical structures but also rapid changes in tissue properties due to myelination and structuring that are reflected in the multi-modal Magnetic Resonance (MR) contrast measurements. We propose a new registration method that generates a mapping between brain anatomies represented as a multi-compartment model of tissue class posterior images and geometries. We transform intensity patterns into combined probabilistic and geometric descriptors that drive the matching in a diffeomorphic framework, where distances between geometries are represented using currents which does not require geometric correspondence. We show preliminary results on the registrations of neonatal brain MRIs to two-year old infant MRIs using class posteriors and surface boundaries of structures undergoing major changes. Quantitative validation demonstrates that our proposed method generates registrations that better preserve the consistency of anatomical structures over time.

The trajectory of early brain development is marked by rapid growth presented by volume but also by tissue property changes. Capturing regional characteristics of axonal structuring and myelination via neuroimaging requires analysis of longitudinal image data with multiple modalities. Complementary to earlier studies of volume and cortical folding analysis, this paper focuses on white matter tissue changes as seen in multimodal MRI and DTI. We propose a new framework for analyzing early maturation in white matter that generates a normative spatiotemporal model and provides 3D maps of absolute and relative indices of maturation. The method, using a continuous model of intensity changes using modified Legendre polynomials, has been applied to a multimodal dataset (T1W, T2W, PD, DTI) with 8 subjects that have been scanned at approximately 2 weeks, 1 year, and 2 years. We demonstrate that spatial maturation maps generated from different modalities capture different properties of white matter growth which might lead to a better understanding of the underlying neurobiology.

CONTEXT: Cerebral cortical volume enlargement has been reported in 2- to 4-year-olds with autism. Little is known about the volume of subregions during this period of development. The amygdala is hypothesized to be abnormal in volume and related to core clinical features in autism. OBJECTIVES: To examine amygdala volume at 2 years with follow-up at 4 years of age in children with autism and to explore the relationship between amygdala volume and selected behavioral features of autism. DESIGN: Longitudinal magnetic resonance imaging study. SETTING: University medical setting. PARTICIPANTS: Fifty autistic and 33 control (11 developmentally delayed, 22 typically developing) children between 18 and 35 months (2 years) of age followed up at 42 to 59 months (4 years) of age. MAIN OUTCOME MEASURES: Amygdala volumes in relation to joint attention ability measured with a new observational coding system, the Social Orienting Continuum and Response Scale; group comparisons including total tissue volume, sex, IQ, and age as covariates. RESULTS: Amygdala enlargement was observed in subjects with autism at both 2 and 4 years of age. Significant change over time in volume was observed, although the rate of change did not differ between groups. Amygdala volume was associated with joint attention ability at age 4 years in subjects with autism. CONCLUSIONS: The amygdala is enlarged in autism relative to controls by age 2 years but shows no relative increase in magnitude between 2 and 4 years of age. A significant association between amygdala volume and joint attention suggests that alterations to this structure may be linked to a core deficit of autism.

Obtaining validation data and comparison metrics for segmentation of magnetic resonance images (MRI) are difficult tasks due to the lack of reliable ground truth. This problem is even more evident for images presenting pathology, which can both alter tissue appearance through infiltration and cause geometric distortions. Systems for generating synthetic images with user-defined degradation by noise and intensity inhomogeneity offer the possibility for testing and comparison of segmentation methods. Such systems do not yet offer simulation of sufficiently realistic looking pathology. This paper presents a system that combines physical and statistical modeling to generate synthetic multi-modal 3D brain MRI with tumor and edema, along with the underlying anatomical ground truth, Main emphasis is placed on simulation of the major effects known for tumor MRI, such as contrast enhancement, local distortion of healthy tissue, infiltrating edema adjacent to tumors, destruction and deformation of fiber tracts, and multi-modal MRI contrast of healthy tissue and pathology. The new method synthesizes pathology in multi-modal MRI and diffusion tensor imaging (DTI) by simulating mass effect, warping and destruction of white matter fibers, and infiltration of brain tissues by tumor cells. We generate synthetic contrast enhanced MR images by simulating the accumulation of contrast agent within the brain. The appearance of the the brain tissue and tumor in MRI is simulated by synthesizing texture images from real MR images. The proposed method is able to generate synthetic ground truth and synthesized MR images with tumor and edema that exhibit comparable segmentation challenges to real tumor MRI. Such image data sets will find use in segmentation reliability studies, comparison and validation of different segmentation methods, training and teaching, or even in evaluating standards for tumor size like the RECIST criteria (response evaluation criteria in solid tumors).