Alejandro V. Levy

Enhanced resting activity of the oral somatosensory cortex in obese subjects.

The cerebral mechanisms underlying excess food intake in obese subjects are poorly understood. We used PET and 2-deoxy-2[18F]fluoro-D-glucose to assess differences in regional brain metabolism between obese and lean subjects at rest. Brain metabolic images were analyzed using statistical parameter maps. We found that obese subjects have significantly higher metabolic activity in the bilateral parietal somatosensory cortex in the regions where sensation to the mouth, lips and tongue are located. The enhanced activity in somatosensory regions involved with sensory processing of food in the obese subjects could make them more sensitive to the rewarding properties of food related to palatability and could be one of the variables contributing to their excess food consumption.

Three-dimensional anatomical model-based segmentation of MR brain images through principal axes registration

Model-based segmentation and analysis of brain images depends on anatomical knowledge which may be derived from conventional atlases. Classical anatomical atlases are based on the rigid spatial distribution provided by a single cadaver. Their use to segment internal anatomical brain structures in a high-resolution MR brain image does not provide any knowledge about the subject variability, and therefore they are not very efficient in analysis. The authors present a method to develop three-dimensional computerized composite models of brain structures to build a computerized anatomical atlas. The composite models are developed using the real MR brain images of human subjects which are registered through the principal axes transformation. The composite models provide probabilistic spatial distributions, which represent the variability of brain structures and can be easily updated for additional subjects. The authors demonstrate the use of such a composite model of ventricular structure to help segmentation of the ventricles and cerebrospinal fluid of MR brain images. Here, a composite model of ventricles using a set of 22 human subjects is developed and used in a model-based segmentation of ventricles, sulci, and white matter lesions. To illustrate the clinical usefulness, automatic volumetric measurements on ventricular size and cortical atrophy for an additional eight alcoholics and 10 normal subjects were made. The volumetric quantitative results indicated regional brain atrophy in chronic alcoholics.<<ETX>>

Iterative principal axes registration method for analysis of MR-PET brain images

Computerized automatic registration of MR-PET images of the brain is of significant interest for multimodality brain image analysis. Here, the authors discuss the principal axes transformation for registration of three-dimensional MR and PET images. A new brain phantom designed to test MR-PET registration accuracy determines that the principal axes registration (PAR) method is accurate to within an average of 1.37 mm with a standard deviation of 0.78 mm. Often the PET scans are not complete in the sense that the PET volume does not match the respective MR volume. The authors have developed an iterative PAR (IPAR) algorithm for such cases. Partial volumes of PET can be accurately registered to the complete MR volume using the new iterative algorithm. The quantitative and qualitative analyses of MR-PET image registration are presented and discussed. Results show that the new PAR algorithm is accurate and practical in MR-PET correlation studies.<<ETX>>

MR-PET image coregistration for quantitation of striatal dopamine D2 receptors.

PURPOSE Our goal was to assess the utility of MR-PET image coregistration to quantify dopamine D2 receptors in striatum. METHOD Twenty-nine normal subjects were investigated with PET and [11C]raclopride and with MRI. D2 receptors were quantified using the ratio of the distribution volume in striatum to that in cerebellum. Measures obtained using regions selected directly from the PET images were compared with those obtained from MR images and then projected to coregistered PET images. RESULTS There were no differences between measures selected from the PET images (3.9 +/- 0.5) and those from the MR images (3.9 +/- 0.65). The values for these two measures were significantly correlated and corresponded to r = 0.9, p < 0.0001. CONCLUSION Regions of interest selected directly from PET images, where there is a large contrast between the region of interest and background, as for the case of dopamine D2 ligands, are almost identical to those obtained from coregistered MR images.

Hypometabolism in olfactory cortical projection areas of male patients with schizophrenia: an initial positron emission tomography study.

Several studies have reported olfactory deficits in schizophrenic patients. This study examines local cerebral metabolic rate within two cortical areas in eight normal men and eight schizophrenic men. A significantly greater degree of hypometabolism was observed in the schizophrenic men in the cortical area of the nondominant hemisphere that receives direct uncrossed olfactory projections.

Intimate combination of low‐ and high‐resolution image data: I. real‐space PET and 1H2O MRI, PETAMRI

Two different types of (co‐registered) images of the same slice of tissue will generally have different spatial resolutions. The judicious pixel‐by‐pixel combination of their data can be accomplished to yield a single image exhibiting properties of both. Here, axial 18FDG PET and 1H2O MR images of the human brain are used as the low‐ and high‐resolution members of the pair. A color scale is necessary in order to provide for separate intensity parameters from the two image types. However, not all color scales can accommodate this separability. The HSV color model allows one to choose a color scale in which the intensity of the low‐resolution image type is coded as hue, while that of the high‐resolution type is coded as value, a reasonably independent parameter. Furthermore, the high‐resolution image must have high contrast and be quantitative in the same sense as the low‐resolution image almost always is. Here, relaxographic MR images (naturally segmented quantitative 1H2O spin‐density components) are used. Their essentially complete contrast serves to effect an apparent editing function when encoded as the value of the color scale. Thus, the combination of 18FDG PET images with gray‐matter (GM) relaxographic 1H2O images produces visually “GM‐edited” 18FDG PETAMR (positron emission tomography and magnetic resonance) images. These exhibit the high sensitivity to tracer amounts characteristic of PET along with the high spatial resolution of 1H2O MRI. At the same time, however, they retain the complete quantitative measures of each of their basis images. Magn Reson Med 42:345–360, 1999. Published 1999 Wiley‐Liss, Inc.

The Spectral Analysis Of Brain Glucose Metabolism

We present a computer method that assists in the analysis and interpretation of metabolic functional patterns occurring in 3-Dimensional human brain images produced by PET and SPECT cameras. We introduce the concept of the Metabolic Spectral Signature, a continuous curve across the metabolic spectrum that objectively generates optimum descriptors of asymmetric metabolic patterns and automatically selects the associated anatomical brain structures.We applied it to the study of asymmetries in PET brain images of normal and schizophrenic subjects and found three narrow bands in the metabolic spectrum, a=[2,3.51, p=[5.5,7.5] and y=[ 12,12.51 (mg glucose/lOO gr/min), where schizophrenic brains have statistically significant asymmetric metabolic patterns with respect to the normal brains.The anatomical structures identified with these spectral bands utilize 14%,35% and 4% of the total metabolic activity in a normal brain.These results suggest that the Metabolic Spectral Signature is a useful approach to classify clinical groups and increase our understanding of the relationship between functional and anatomical systems in the brain and their disruption by mental illness or drug abuse.

Signal analysis of human brain function activation: effect of spatial registration noise in brain images

Human brain function is frequently being studied by subtracting two brain images of the same subject, one obtained while resting and the other one while his brain is activated by either a motor task, a cognitive activity, the effects of a medication, or drug abuse. The activation signal has noise induced both by the camera and the patient, i.e. daily metabolic variability, patient positioning errors, etc. The present methodology allows the determination of faint activation signals by finding sharper error bounds in the error induced in the signal by each separate source of noise in the image, combining them later for a final bound on the noise to assess the signals statistical significance. Using simple concepts from numerical analysis and directional statistics, we present a general methodology to analyze the effect that spatial registration errors have on each pixels signal to noise ratio. This approach to signal analysis is valid for most imaging modalities and registration methods. Experimental results are given for 48 subjects, using Positron Emission Tomography (PET) brain images spatially registered with the Centroidal Principal Axes registration method.<<ETX>>

The galaxy method for the 3-D spatial registration of PET images to Talairach brain atlas

Human brain function is studied by averaging many PET images in order to enhance the signal to noise ratio of weak group-specific patterns caused by medication, illness, or functional activation. Present registration methods by Woods and Friston (SPM), compute 3 translations and 3 rotations to register each PET image to a reference anatomical atlas using a 6 degree of freedom optimization method. By using only a 2 degree of freedom optimization method, the present method decreases the computing time down to 70 sec in a SUN Sparc2 CPU; this is a speed increment by a factor of 3 to 15 times over previous methods. Experimental results for 24 FDG-PET images also indicate that the present method improves the accuracy of the registration to a mean error of 0.48 mm and a maximum error of 2.47 mm.