Gee, Jim C
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PublicationEfficient Generation of Shape-Based Reference Frames for the Corpus Callosum for DTI-based Connectivity Analysis(2006-06-01) Sun, Hui; Yushkevich, Paul A; Gee, James C.; Yushkevich, Paul A; Zhang, Hui; Gee, James C.; Simon, Tony JYushkevich et.al. [17, 18] established a PDE-based deformable modeling approach called continuous medial representation (cm-rep), in which the geometric relationship between the medial axis of a 3D object and its boundary is captured. Continuous medial description of an object not only provides useful shape features for object characterization and comparison; it also imposes a shape-based reference frame on the interior of that object. Such a reference frame provides a useful means of representing different instances of an anatomical structure using a common canonical parametrization domain. This paper presents an efficient method to construct continuous medial shape models for 2D objects. A closed form solution for the ordinary differential equation (ODE) is derived via Pythagorean hodograph (PH) curves. That closed form solution reduces the computation complexity from solving an ODE system to pure algebraic manipulation. Using this method, we generate shape-based reference frames, and demonstrate how they can be applied to the analysis of anatomical connectivity of corpora callosa, obtained by fiber tracking in diffusion tensor magnetic resonance imaging (DTI) in a chromosome 22q11.2 deletion syndrome study. PublicationMulti-start Method with Prior Learning for Image Registration(2007-10-10) Song, Gang; Avants, Brian B; Gee, Jim C.; Song, Gang; Avants, Brian B; Gee, Jim C.We propose an efficient image registration strategy that is based on learned prior distributions of transformation parameters. These priors are used to constrain a finite- time multi-start optimization method. Motivation for this approach comes from the fact that standard affine brain image registration methods, especially those based on gradient descent optimization alone, are affected by the initial search position. While global optimization methods can resolve this problem, they are are often very time consuming. Our goal is to build an explicit prior model of the gap between a typical registration solution and the solution gained by a global optimization method. We use this learned prior model to restrict randomized search in the relevant parameter space surrounding the initial solution. Global optimization in this restricted parameter space provides, in finite time, results that are superior to both gradient descent and the general multi-start strategy. The performance of our method is illustrated on a data set of 67 elderly and neurodegenerative brains. Our novel learning strategy and the associated registration method are shown to outperform other approaches. Theoretical, synthetic and real-world examples illustrate this improvement. PublicationMultivariate Normalization with Symmetric Diffeomorphisms for Multivariate Studies(2007-11-22) Avants, Brian B; Duda, Jeffrey T; Gee, James C.; Gee, James C.Current clinical and research neuroimaging protocols acquire images using multiple modalities, for instance, T1, T2, diffusion tensor and cerebral blood flow magnetic resonance images (MRI). These multivariate datasets provide unique and often complementary anatomical and physiological information about the subject of interest. We present a method that uses fused multiple modality (scalar and tensor) datasets to perform intersubject spatial normalization. Our multivariate approach has the potential to eliminate inconsistencies that occur when normalization is performed on each modality separately. Furthermore, the multivariate approach uses a much richer anatomical and physiological image signature to infer image correspondences and perform multivariate statistical tests. In this initial study, we develop the theory for Multivariate Symmetric Normalization (MVSyN), establish its feasibility and discuss preliminary results on a multivariate statistical study of 22q deletion syndrome. PublicationTopological Repairing of 3D Digital Images(2008-03-01) Siqueira, Marcelo; Gallier, Jean H; Gee, James C.; Gallier, Jean H; Gee, James C.We present here a new randomized algorithm for repairing the topology of objects represented by 3D binary digital images. By "repairing the topology", we mean a systematic way of modifying a given binary image in order to produce a similar binary image which is guaranteed to be well-composed. A 3D binary digital image is said to be well-composed if, and only if, the square faces shared by background and foreground voxels form a 2D manifold. Well-composed images enjoy some special properties which can make such images very desirable in practical applications. For instance, well-known algorithms for extracting surfaces from and thinning binary images can be simplified and optimized for speed if the input image is assumed to be well-composed. Furthermore, some algorithms for computing surface curvature and extracting adaptive triangulated surfaces, directly from the binary data, can only be applied to well-composed images. Finally, we introduce an extension of the aforementioned algorithm to repairing 3D digital multivalued images. Such an algorithm finds application in repairing segmented images resulting from multi-object segmentations of other 3D digital multivalued images. PublicationElastically Deforming a Three-Dimensional Atlas to Match Anatomical Brain Images(1993-10-01) Gee, James C; Reivich, Martin; Gee, James C; Reivich, Martin; Bajcsy, RuzenaTo evaluate our system for elastically deforming a three-dimensional atlas to match anatomical brain images, six deformed versions of an atlas were generated. The deformed atlases were created by elastically mapping an anatomical brain atlas onto different MRI brain image volumes. The mapping matches the edges of the ventricles and the surface of the brain; the resultant deformations are propagated through the atlas volume, deforming the remainder of the structures in the process. The atlas was then elastically matched to its deformed versions. The accuracy of the resultant matches was evaluated by determining the correspondence of 32 cortical and subcortical structures. The system on average matched the centroid of a structure to within 1 mm of its true position and fit a structure to within 11% of its true volume. The overlap between the matched and true structures, defined by the ratio between the volume of their intersection and the volume of their union, averaged 66%. When the gray-white interface was included for matching, the mean overlap improved to 78%; each structure was matched to within 0.6 mm of its true position and fit to within 6% of its true volume. Preliminary studies were also made to determine the effect of the compliance of the atlas on the resultant match. PublicationCanine and Human Visual Cortex Intact and Responsive Despite Early Retinal Blindness from RPE65 Mutation(2007-06-26) Aguirre, Geoffrey K; Komáromy, András M; Cideciyan, Artur V; Brainard, David H; Aleman, Tomas S; Avants, Brian B; Gee, James C; Aguirre, Geoffrey K; Komáromy, András M; Aguirre, Gustavo D; Cideciyan, Artur V; Jacobson, Samuel G; Brainard, David H; Aleman, Tomas S; Roman, Alejandro J; Avants, Brian B; Gee, James C; Korczykowski, Marc; Hauswirth, William W; Acland, Gregory M; Jacobson, Samuel GBackground RPE65 is an essential molecule in the retinoid-visual cycle, and RPE65 gene mutations cause the congenital human blindness known as Leber congenital amaurosis (LCA). Somatic gene therapy delivered to the retina of blind dogs with an RPE65 mutation dramatically restores retinal physiology and has sparked international interest in human treatment trials for this incurable disease. An unanswered question is how the visual cortex responds after prolonged sensory deprivation from retinal dysfunction. We therefore studied the cortex of RPE65-mutant dogs before and after retinal gene therapy. Then, we inquired whether there is visual pathway integrity and responsivity in adult humans with LCA due to RPE65 mutations (RPE65-LCA). Methods and Findings RPE65-mutant dogs were studied with fMRI. Prior to therapy, retinal and subcortical responses to light were markedly diminished, and there were minimal cortical responses within the primary visual areas of the lateral gyrus (activation amplitude mean ± standard deviation [SD] = 0.07% ± 0.06% and volume = 1.3 ± 0.6 cm3). Following therapy, retinal and subcortical response restoration was accompanied by increased amplitude (0.18% ± 0.06%) and volume (8.2 ± 0.8 cm3) of activation within the lateral gyrus (p < 0.005 for both). Cortical recovery occurred rapidly (within a month of treatment) and was persistent (as long as 2.5 y after treatment). Recovery was present even when treatment was provided as late as 1–4 y of age. Human RPE65-LCA patients (ages 18–23 y) were studied with structural magnetic resonance imaging. Optic nerve diameter (3.2 ± 0.5 mm) was within the normal range (3.2 ± 0.3 mm), and occipital cortical white matter density as judged by voxel-based morphometry was slightly but significantly altered (1.3 SD below control average, p = 0.005). Functional magnetic resonance imaging in human RPE65-LCA patients revealed cortical responses with a markedly diminished activation volume (8.8 ± 1.2 cm3) compared to controls (29.7 ± 8.3 cm3, p < 0.001) when stimulated with lower intensity light. Unexpectedly, cortical response volume (41.2 ± 11.1 cm3) was comparable to normal (48.8 ± 3.1 cm3, p = 0.2) with higher intensity light stimulation. Conclusions Visual cortical responses dramatically improve after retinal gene therapy in the canine model of RPE65-LCA. Human RPE65-LCA patients have preserved visual pathway anatomy and detectable cortical activation despite limited visual experience. Taken together, the results support the potential for human visual benefit from retinal therapies currently being aimed at restoring vision to the congenitally blind with genetic retinal disease. PublicationA Digital Atlas of the Dog Brain(2012-12-20) Datta, Ritobrato; Lee, Jongho; Avants, Brian B; Vite, Charles H; Datta, Ritobrato; Gee, Jim C; Lee, Jongho; Aguirre, Geoffrey K; Aguirre, Gustavo D; Duda, Jeffrey; Avants, Brian B; Vite, Charles H; Tseng, Ben; Gee, Jim C; Aguirre, Geoffrey KThere is a long history and a growing interest in the canine as a subject of study in neuroscience research and in translational neurology. In the last few years, anatomical and functional magnetic resonance imaging (MRI) studies of awake and anesthetized dogs have been reported. Such efforts can be enhanced by a population atlas of canine brain anatomy to implement group analyses. Here we present a canine brain atlas derived as the diffeomorphic average of a population of fifteen mesaticephalic dogs. The atlas includes: 1) A brain template derived from in-vivo, T1-weighted imaging at 1 mm isotropic resolution at 3 Tesla (with and without the soft tissues of the head); 2) A co-registered, high-resolution (0.33 mm isotropic) template created from imaging of ex-vivo brains at 7 Tesla; 3) A surface representation of the gray matter/white matter boundary of the high-resolution atlas (including labeling of gyral and sulcal features). The properties of the atlas are considered in relation to historical nomenclature and the evolutionary taxonomy of the Canini tribe. The atlas is available for download (https://cfn.upenn.edu/aguirre/wiki/public:data_plosone_2012_datta).