Magnetic resonance image tissue classification using a partial volume model.

We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average kappa indices of kappa = 0.746 +/- 0.114 for gray matter (GM) and kappa = 0.798 +/- 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average kappa indices kappa = 0.893 +/- 0.041 for GM and kappa = 0.928 +/- 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute's BrainWeb phantom.

Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects.

The desire to correct intensity nonuniformity in magnetic resonance images has led to the proliferation of nonuniformity-correction (NUC) algorithms with different theoretical underpinnings. In order to provide end users with a rational basis for selecting a given algorithm for a specific neuroscientific application, we evaluated the performance of six NUC algorithms. We used simulated and real MRI data volumes, including six repeat scans of the same subject, in order to rank the accuracy, precision, and stability of the nonuniformity corrections. We also compared algorithms using data volumes from different subjects and different (1.5T and 3.0T) MRI scanners in order to relate differences in algorithmic performance to intersubject variability and/or differences in scanner performance. In phantom studies, the correlation of the extracted with the applied nonuniformity was highest in the transaxial (left-to-right) direction and lowest in the axial (top-to-bottom) direction. Two of the six algorithms demonstrated a high degree of stability, as measured by the iterative application of the algorithm to its corrected output. While none of the algorithms performed ideally under all circumstances, locally adaptive methods generally outperformed nonadaptive methods.

Comparison of voxel- and volume-of-interest-based analyses in FDG PET scans of HIV positive and healthy individuals.

UNLABELLED: Abnormal glucose metabolic patterns in the brain have been reported for HIV-1 seropositive (HIV+) patients when compared with seronegative healthy individuals. The metabolic covariance pattern obtained from voxel- or volume-of-interest (VOI)-based multivariate data analysis techniques can be used to characterize disease and potentially to detect and monitor disease progression in the early stage of AIDS dementia complex. However, the arbitrary smoothing typically applied to PET data during reconstruction and processing to reduce noise has an unknown effect on the data, especially for the voxel-based analysis. METHODS: To investigate the impact of smoothing on a discrimination task, we applied principal component analysis with scaled subprofile-model preprocessing (SSM/PCA) followed by Fisher discriminant analysis to FDG PET data that were reconstructed and processed with different degrees of smoothing. Receiver operating characteristic curves were used to compare the ability of derived metabolic covariance patterns to discriminate HIV+ patients from healthy volunteers. RESULTS: For the voxel-based analysis, we found that the spatial distribution of voxel weights from the SSM/PCA analysis suggested edge effects along major tissue and cerebrospinal fluid boundaries, indicative of a disease-specific pattern of cerebral atrophy for the HIV+ patients. In terms of its discrimination performance, this metabolic covariance pattern is stable and insensitive to a wide range of smoothing kernels, except for ramp reconstruction and Hanning reconstruction with 7 x 7 x 7 block smoothing. In these reconstructions, the discrimination performance decreased as a result of high image noise and excessive smoothing, respectively. Our results also indicated that if sufficient variance from the VOI measurements is included, the overall performance of a conventional VOI-based analysis can be similar to that of the voxel-based analysis for the same discrimination task. However, the VOI-based analysis performed poorly at low false-positive fraction and is less tolerant to noise in the metabolic covariance pattern than the voxel-based analysis. CONCLUSION: We have obtained a unique covariance pattern of brain glucose metabolism for HIV+ patients compared with healthy volunteers. Discrimination based on this covariance pattern was found to be insensitive to a wide range of image smoothness.

A symbolic environment for visualizing activated foci in functional neuroimaging datasets.

This paper presents a symbolic visualization environment known as the Corner Cube environment, which was developed to facilitate rapid examination and comparison of activated foci defined by analyses of functional neuroimaging datasets. We have performed a comparative evaluation of this environment against maximum-intensity projection and 'gallery of slices' displays, and the results suggest that the Corner Cube environment has definite advantages over both conventional display techniques. We conclude that the Corner Cube is an effective tool for summarizing the spatial characteristics of activated foci within an easily understood visual context and is especially useful for displaying the similarities and differences in functional neuroimaging datasets.

Activation pattern reproducibility: measuring the effects of group size and data analysis models.

The reproducibility of patterns from brain activation experiments has been examined only for suprathreshold spatially localized foci. Scatter plots comparing signal levels across all pairs of Talairach voxels for pairs of functional activation images provide an alternative approach for assessing reproducibility. Image-wide, signal-level reproducibility may be quantitatively summarized using pattern similarity measures such as the Pearson product-moment correlation, rho. Empirical population distributions of rho for many pair-wise image comparisons, generated using statistical resampling techniques, may be used to examine the impact of a wide range of experimental variables. We demonstrate the use of such empirical rho-histograms to measure changes in reproducibility for [(15)O]-water PET scans of a simple motor task as a function of group size and data analysis model.

Abnormal cerebral glucose metabolism in HIV-1 seropositive subjects with and without dementia.

UNLABELLED: This study was undertaken in order to extend our previous finding of relative basal ganglia hypermetabolism in AIDS dementia complex (ADC) and to develop clinically useful metabolic indices of CNS involvement in HIV-seropositive (HIV+) subjects. METHODS: Twenty-one HIV+ subjects (11 with AIDS) underwent FDG-PET scanning; 12 had a follow-up scan at 6 mo and 4 had a third scan at 12 mo. Forty-three age-matched heterosexual volunteers served as controls. FDG-PET scanning was performed with arterial blood sampling, and scan data were analyzed using the Scaled Subprofile Model (SSM) with principal component analysis. RESULTS: SSM/principal component analysis of the combined (HIV+ and controls) FDG-PET dataset extracted two major disease-related metabolic components: (a) a nonspecific indicator of cerebral dysfunction, which was significantly correlated with age, cerebral atrophy and ADC stage and (b) the striatum, which was heavily weighted (relatively hypermetabolic) and appeared to provide a disease-specific measure of early CNS involvement. CONCLUSION: FDG-PET scans provide quantitative measures of abnormal functional connectivity in HIV-seropositives-with or without AIDS or ADC. These measures, which are robust across centers with respect to instrumentation, scanning technique and disease severity, appear to track the progression of CNS involvement in patients with subclinical neurologic or neuropsychologic dysfunction.

Principal component analysis and the scaled subprofile model compared to intersubject averaging and statistical parametric mapping: I. "Functional connectivity" of the human motor system studied with [15O]water PET.

Using [15O]water PET and a previously well studied motor activation task, repetitive finger-to-thumb opposition, we compared the spatial activation patterns produced by (1) global normalization and intersubject averaging of paired-image subtractions, (2) the mean differences of ANCOVA-adjusted voxels in Statistical Parametric Mapping, (3) ANCOVA-adjusted voxels followed by principal component analysis (PCA), (4) ANCOVA-adjustment of mean image volumes (mean over subjects at each time point) followed by F-masking and PCA, and (5) PCA with Scaled Subprofile Model pre- and postprocessing. All data analysis techniques identified large positive focal activations in the contralateral sensorimotor cortex and ipsilateral cerebellar cortex, with varying levels of activation in other parts of the motor system, e.g., supplementary motor area, thalamus, putamen; techniques 1-4 also produced extensive negative areas. The activation signal of interest constitutes a very small fraction of the total nonrandom signal in the original dataset, and the exact choice of data preprocessing steps together with a particular analysis procedure have a significant impact on the identification and relative levels of activated regions. The challenge for the future is to identify those preprocessing algorithms and data analysis models that reproducibly optimize the identification and quantification of higher-order sensorimotor and cognitive responses.

Display of merged multimodality brain images using interleaved pixels with independent color scales.

UNLABELLED: This article reviews common methods for two-dimensional display of registered multimodality brain images and describes a software package for presentation of merged MRI and PET images that runs on a workstation with an eight-bit color display. The software package displays merged brain images from multiple modalities in a way that is readily manipulated, visually pleasing and easy to interpret. The display method used, i.e., interleaving of alternate pixels with independent color scales, is effective in producing merged images with high contrast-detail for each volume. Interleaving images from different volumes creates unusual perceptual effects, one of which is the apparent camouflage of low-contrast signals by high values in the paired volume. METHODS: The camouflage effect was thought to arise from perceptual merging of adjacent pixels. An observer experiment was conducted to investigate this tendency of high-activity PET data to obscure low-contrast detail in interleaved MRI data in spite of the digital independence of neighboring pixels. Four observers were presented with 20 combinations of signal plus background targets with uniform mask images, using a two-alternative forced-choice experimental design with 50 trials per combination. RESULTS: The psychophysical evaluation of the ability of human observers to detect the simple test objects in an interleaved image presentation indicated a statistically significant camouflage effect of one volume on the other for some combinations of target and mask. The concept of perceptual merging of adjacent pixels was able to predict which combinations caused the greatest degradations in performance. CONCLUSIONS: The image interleaving approach to the display of two-dimensional slices from registered image volumes makes efficient use of an eight-bit color display. Contrast resolution of both individual volumes is high compared with that in other techniques and the volumes are presented in familiar color scales. However, the method yields an unexpected camouflage effect that tends to obscure low-contrast signals. The practical effect of such camouflage on the interpretation of clinical images remains to be investigated.

Graphical analysis of MR feature space for measurement of CSF, gray-matter, and white-matter volumes.

The problem of volume averaging in quantitating CSF, gray-matter, and white-matter fractions in the brain is solved using a three-compartment model and a simple graphical analysis of a multispectral MR feature space. Compartmentalization is achieved without the ambiguities of thresholding techniques or the need to assume that the underlying pixel probability distributions have a particular form. A 2D feature space is formed by double SE (proton density- and T2-weighted) MR data with image nonuniformity removed by a novel technique in which the brain itself serves as a uniformity reference. Compartments other than the basic three were rejected by the tailoring of limits in feature space. Phantom scans substantiate this approach, and the importance of the careful selection and standardization of pure tissue reference signals is demonstrated. Compartmental profiles from standardized subvolumes of three normal brains, based on a 3D (Talairach) coordinate system, demonstrate slice-by-slice detail; longitudinal studies confirm reproducibility. Compartmentalization may be described graphically and algebraically, complementing data displays in feature space and images of compartmentalized brain scans. These studies anticipate the application of our compartmentalization technique to patients with neurological disorders.