BACKGROUND: Advanced neuroimaging techniques have brought increasing recognition of cerebellar injury among premature infants. The developmental relationship between early brain injury and effects on the cerebrum and cerebellum remains unclear. OBJECTIVES: To examine whether cerebral parenchymal brain lesions among preterm infants are associated with subsequent decreases in cerebellar volume and, conversely, whether primary cerebellar injury is associated with decreased cerebral brain volumes, with advanced, 3-dimensional, volumetric MRI at term gestational age equivalent. METHODS: Total cerebellar volumes and cerebellar gray and myelinated white matter volumes were determined through manual outlining for 74 preterm infants with unilateral periventricular hemorrhagic infarction (14 infants), bilateral diffuse periventricular leukomalacia (20 infants), cerebellar hemorrhage (10 infants), or normal term gestational age equivalent MRI findings (30 infants). Total brain and right/left cerebral and cerebellar hemispheric volumes were calculated. RESULTS: Unilateral cerebral brain injury was associated with significantly decreased volume of the contralateral cerebellar hemisphere. Conversely, unilateral primary cerebellar injury was associated with a contralateral decrease in supratentorial brain volume. Cerebellar gray matter and myelinated white matter volumes were reduced significantly not only among preterm infants with primary cerebellar hemorrhage but also among infants with cerebral parenchymal brain injury. CONCLUSIONS: These data suggest strongly that both reduction in contralateral cerebellar volume with unilateral cerebral parenchymal injury and reduction in total cerebellar volume with bilateral cerebral lesions are related to trophic transsynaptic effects. Early-life cerebellar injury may contribute importantly to the high rates of cognitive, behavioral, and motor deficits reported for premature infants.

In this paper, we propose an expectation-maximization (EM) approach to separate a shape database into different shape classes, while simultaneously estimating the shape contours that best exemplify each of the different shape classes. We begin our formulation by employing the level set function as the shape descriptor. Next, for each shape class we assume that there exists an unknown underlying level set function whose zero level set describes the contour that best represents the shapes within that shape class. The level set function for each example shape in the database is modeled as a noisy measurement of the appropriate shape class’s unknown underlying level set function. Based on this measurement model and the judicious introduction of the class labels as the hidden data, our EM formulation calculates the labels for shape classification and estimates the shape contours that best typify the different shape classes. This resulting iterative algorithm is computationally efficient, simple, and accurate. We demonstrate the utility and performance of this algorithm by applying it to two medical applications.

Diffusion tensor imaging (DTI) studies in schizophrenia demonstrate lower anisotropic diffusion within white matter due either to loss of coherence of white matter fiber tracts, to changes in the number and/or density of interconnecting fiber tracts, or to changes in myelination, although methodology as well as localization of such changes differ between studies. The aim of this study is to localize and to specify further DTI abnormalities in schizophrenia by combining DTI with magnetization transfer imaging (MTI), a technique sensitive to myelin and axonal alterations in order to increase specificity of DTI findings. 21 chronic schizophrenics and 26 controls were scanned using Line-Scan-Diffusion-Imaging and T1-weighted techniques with and without a saturation pulse (MT). Diffusion information was used to normalize co-registered maps of fractional anisotropy (FA) and magnetization transfer ratio (MTR) to a study-specific template, using the multi-channel daemon algorithm, designed specifically to deal with multidirectional tensor information. Diffusion anisotropy was decreased in schizophrenia in the following brain regions: the fornix, the corpus callosum, bilaterally in the cingulum bundle, bilaterally in the superior occipito-frontal fasciculus, bilaterally in the internal capsule, in the right inferior occipito-frontal fasciculus and the left arcuate fasciculus. MTR maps demonstrated changes in the corpus callosum, fornix, right internal capsule, and the superior occipito-frontal fasciculus bilaterally; however, no changes were noted in the anterior cingulum bundle, the left internal capsule, the arcuate fasciculus, or inferior occipito-frontal fasciculus. In addition, the right posterior cingulum bundle showed MTR but not FA changes in schizophrenia. These findings suggest that, while some of the diffusion abnormalities in schizophrenia are likely due to abnormal coherence, or organization of the fiber tracts, some of these abnormalities may, in fact, be attributed to or coincide with myelin/axonal disruption.

We describe a new algorithm for non-rigid registration capable of estimating a constrained dense displacement field from multi-modal image data. We applied this algorithm to capture non-rigid deformation between digital images of histological slides and digital flat-bed scanned images of cryotomed sections of the larynx, and carried out validation experiments to measure the effectiveness of the algorithm. The implementation was carried out by extending the open-source Insight ToolKit software. In diagnostic imaging of cancer of the larynx, imaging modalities sensitive to both anatomy (such as MRI and CT) and function (PET) are valuable. However, these modalities differ in their capability to discriminate the margins of tumor. Gold standard tumor margins can be obtained from histological images from cryotomed sections of the larynx. Unfortunately, the process of freezing, fixation, cryotoming and staining the tissue to create histological images introduces non-rigid deformations and significant contrast changes. We demonstrate that the non-rigid registration algorithm we present is able to capture these deformations and the algorithm allows us to align histological images with scanned images of the larynx. Our non-rigid registration algorithm constructs a deformation field to warp one image onto another. The algorithm measures image similarity using a mutual information similarity criterion, and avoids spurious deformations due to noise by constraining the estimated deformation field with a linear elastic regularization term. The finite element method is used to represent the deformation field, and our implementation enables us to assign inhomogeneous material characteristics so that hard regions resist internal deformation whereas soft regions are more pliant. A gradient descent optimization strategy is used and this has enabled rapid and accurate convergence to the desired estimate of the deformation field. A further acceleration in speed without cost of accuracy is achieved by using an adaptive mesh refinement strategy.

The aim of this study was the morphological and further chemical characterisation of neurons immunoreactive for leu-enkephalin (leuENK). Ten wholemounts of small and large intestinal segments from nine patients were immunohistochemically triple-stained for leuENK/neurofilament 200 (NF)/substance P (SP). Based on their simultaneous NF-reactivity and 3D reconstruction of single NF-reactive cells, 97.5% of leuENK-positive neurons displayed the appearance of stubby neurons: small somata; short, stubby dendrites and one axon. Of these leuENK-reactive stubby neurons, 91.3% did not display co-reactivity for SP whereas 8.7% were SP-co-reactive. As to their axonal projection pattern, 50.4% of the recorded leuENK stubby neurons had axons running orally whereas in 29.4% they ran anally; the directions of the remaining 20.2% could not be determined. No axons were seen to enter into secondary strands of the myenteric plexus. Somal area measurements revealed clearly smaller somata of leuENK-reactive stubby neurons (between 259+/-47 microm(2) and 487+/-113 microm(2)) than those of putative sensory type II neurons (between 700+/-217 microm(2) and 1,164+/-396 microm(2)). The ratio dendritic field area per somal area of leuENK-reactive stubby neurons was between 2.0 and 2.8 reflecting their short dendrites. Additionally, we estimated the proportion of leuENK-positive neurons in comparison to the putative whole myenteric neuron population in four leuENK/anti-Hu doublestained wholemounts. This proportion ranged between 5.9% and 8.3%. We suggest leuENK-reactive stubby neurons to be muscle motor neurons and/or ascending interneurons. Furthermore, we explain why we do not use the term "Dogiel type I neurons" for this population.