Neuroanatomic Markers of Posttraumatic Epilepsy Based on MR Imaging and Machine Learning.

BACKGROUND AND PURPOSE: Although posttraumatic epilepsy is a common complication of traumatic brain injury, the relationship between these conditions is unclear and early posttraumatic epilepsy detection and prevention remain major unmet clinical challenges. This study aimed to identify imaging biomarkers that predict posttraumatic epilepsy among survivors of traumatic brain injury on the basis of an MR imaging data set. MATERIALS AND METHODS: We performed tensor-based morphometry to analyze brain-shape changes associated with traumatic brain injury and to derive imaging features for statistical group comparison. Additionally, machine learning was used to identify structural anomalies associated with brain lesions. Automatically generated brain lesion maps were used to identify brain regions where lesion load may indicate an increased incidence of posttraumatic epilepsy. We used 138 non-posttraumatic epilepsy subjects for training the machine learning method. Validation of lesion delineation was performed on 15 subjects. Group analysis of the relationship between traumatic brain injury and posttraumatic epilepsy was performed on an independent set of 74 subjects (37 subjects with and 37 randomly selected subjects without epilepsy). RESULTS: We observed significant F-statistics related to tensor-based morphometry analysis at voxels close to the pial surface, which may indicate group differences in the locations of edema, hematoma, or hemorrhage. The results of the F-test on lesion data showed significant differences between groups in both the left and right temporal lobes. We also saw significant differences in the right occipital lobe and cerebellum. CONCLUSIONS: Statistical analysis suggests that lesions in the temporal lobes, cerebellum, and the right occipital lobe are associated with an increased posttraumatic epilepsy incidence.

Individual parcellation of resting fMRI with a group functional connectivity prior.

Cortical parcellation based on resting fMRI is an important tool for investigating the functional organization and connectivity of the cerebral cortex. Group parcellation based on co-registration of anatomical images to a common atlas will inevitably result in errors in the locations of the boundaries of functional parcels when they are mapped back from the atlas to the individual. This is because areas of functional specialization vary across individuals in a manner that cannot be fully determined from the sulcal and gyral anatomy that is used for mapping between atlas and individual. We describe a method that avoids this problem by refining an initial group parcellation so that for each subject the parcel boundaries are optimized with respect to that subject's resting fMRI. Initialization with a common parcellation results in automatic correspondence between parcels across subjects. Further, by using a group sparsity constraint to model connectivity, we exploit group similarities in connectivity between parcels while optimizing their boundaries for each individual. We applied this approach with initialization on both high and low density group cortical parcellations and used resting fMRI data to refine across a group of individuals. Cross validation studies show improved homogeneity of resting activity within the refined parcels. Comparisons with task-based localizers show consistent reduction of variance of statistical parametric maps within the refined parcels relative to the group-based initialization indicating improved delineation of regions of functional specialization. This method enables a more accurate estimation of individual subject functional areas, facilitating group analysis of functional connectivity, while maintaining consistency across individuals with a standardized topological atlas.

Automated MRI Volumetric Analysis in Patients with Rasmussen Syndrome.

BACKGROUND AND PURPOSE: Rasmussen syndrome, also known as Rasmussen encephalitis, is typically associated with volume loss of the affected hemisphere of the brain. Our aim was to apply automated quantitative volumetric MR imaging analyses to patients diagnosed with Rasmussen encephalitis, to determine the predictive value of lobar volumetric measures and to assess regional atrophy differences as well as monitor disease progression by using these measures. MATERIALS AND METHODS: Nineteen patients (42 scans) with diagnosed Rasmussen encephalitis were studied. We used 2 control groups: one with 42 age- and sex-matched healthy subjects and the other with 42 epileptic patients without Rasmussen encephalitis with the same disease duration as patients with Rasmussen encephalitis. Volumetric analysis was performed on T1-weighted images by using BrainSuite. Ratios of volumes from the affected hemisphere divided by those from the unaffected hemisphere were used as input to a logistic regression classifier, which was trained to discriminate patients from controls. Using the classifier, we compared the predictive accuracy of all the volumetric measures. These ratios were used to further assess regional atrophy differences and correlate with epilepsy duration. RESULTS: Interhemispheric and frontal lobe ratios had the best prediction accuracy for separating patients with Rasmussen encephalitis from healthy controls and patient controls without Rasmussen encephalitis. The insula showed significantly more atrophy compared with all the other cortical regions. Patients with longitudinal scans showed progressive volume loss in the affected hemisphere. Atrophy of the frontal lobe and insula correlated significantly with epilepsy duration. CONCLUSIONS: Automated quantitative volumetric analysis provides accurate separation of patients with Rasmussen encephalitis from healthy controls and epileptic patients without Rasmussen encephalitis, and thus may assist the diagnosis of Rasmussen encephalitis. Volumetric analysis could also be included as part of follow-up for patients with Rasmussen encephalitis to assess disease progression.

An invariant shape representation using the anisotropic Helmholtz equation.

Analyzing geometry of sulcal curves on the human cortical surface requires a shape representation invariant to Euclidean motion. We present a novel shape representation that characterizes the shape of a curve in terms of a coordinate system based on the eigensystem of the anisotropic Helmholtz equation. This representation has many desirable properties: stability, uniqueness and invariance to scaling and isometric transformation. Under this representation, we can find a point-wise shape distance between curves as well as a bijective smooth point-to-point correspondence. When the curves are sampled irregularly, we also present a fast and accurate computational method for solving the eigensystem using a finite element formulation. This shape representation is used to find symmetries between corresponding sulcal shapes between cortical hemispheres. For this purpose, we automatically generate 26 sulcal curves for 24 subject brains and then compute their invariant shape representation. Left-right sulcal shape symmetry as measured by the shape representation's metric demonstrates the utility of the presented invariant representation for shape analysis of the cortical folding pattern.

Tomato spotted wilt virus Found in Five Species of the genus Tragopogon in a Florida Greenhouse.

An obviously unhealthy plant identified as Tragopogon mirus Ownbey (remarkable goatsbeard) was sent for diagnosis to the Division of Plant Industry (DPI), Gainesville, FL in May of 2008. T. mirus is a recently formed allotetraploid that has T. dubius Scop. and T. porrifolius L. (goatsbeard or salsify) as parents. The parents (family Asteraceae) are diploid and originate from Eurasia. They were introduced to the northwest United States in the early 1900s. The allotetraploid T. mirus, which does not occur in Eurasia, was discovered in 1949 and named in 1950. It has been found in the northwest states of Washington and Idaho. It has also been found in Arizona (4). The plant sent to the DPI was grown in a greenhouse for research purposes at the Botany Department of the University of Florida (Alachua County). Symptoms exhibited on the leaves included mottling, chlorotic and necrotic spots, and mild distortion. Epidermal leaf strips from a mottled leaf were stained with the Orange-Green protein stain and Azure A nucleic acid stain (1). With a light microscope, granular inclusions typical for Tomato spotted wilt virus (TSWV) (1) were seen in leaf strips from both stains. The remainder of the leaf was ground in buffer and tested serologically for TSWV by TSWV-specific ImmunoStrips (Agdia, Elkhart, IN). The ImmunoStrip was positive for the presence of TSWV. This test was confirmed by double-antibody sandwich-ELISA using antiserum and conjugate for TSWV (Agdia). Further serological testing of other Tragopogon species with similar symptoms growing in the same greenhouse revealed that T. miscellus (another recently formed allotetraploid found in the northwestern United States; parents T. dubius and T. pratensis), T. dubius, T. porrifolius, and T. pratensis were also infected with TSWV. Total RNA was extracted from symptomatic leaves of T. mirus, T. dubius, T. porrifolius, and T. miscellus. Reverse transcription-PCR was performed with universal tospovirus primers BR60 and BR65 that amplify part of the nucleocapsid protein gene (2). Target amplicons of 454 bp were produced for all four samples. The PCR product from T. porrifolius was cloned and sequenced. The resulting sequence (GenBank Accession No. FJ655913) shows high homology, 98%, to several isolates of the Tomato spotted wilt virus deposited in the GenBank (Accession Nos. AY870391, AY744477, and AF020659). T. porrifolius has been reported to be naturally infected with TSWV in Italy (3); however, to our knowledge, this is the first report of this virus in the allotetraploids T. mirus and T. miscellus and in the diploids T. dubius and T. pratensis. This report adds five new Asteraceae weeds to the list of possible reservoirs of TSWV in the United States. References: (1) J. R. Edwardson and R. G. Christie. Univ. Fla. Inst. Food Agric. Sci. Bull. 894. 1996. (2) M. Eiras et al. Fitopatol. Bras. 26:170, 2001. (3) G. Parrella et al. J. Plant Pathol. 85:227. 2003. (4) D. E. Soltis et al. Biol. J. Linn. Soc. 82:2004.

POSTURE MATCHING AND ELASTIC REGISTRATION OF A MOUSE ATLAS TO SURFACE TOPOGRAPHY RANGE DATA.

Estimation of internal mouse anatomy is required for quantitative bioluminescence or fluorescence tomography. However, only surface range data can be recovered from all-optical systems. These data are at times sparse or incomplete. We present a method for fitting an elastically deformable mouse atlas to surface topographic range data acquired by an optical system. In this method, we first match the postures of a deformable atlas and the range data of the mouse being imaged. This is achieved by aligning manually identified landmarks. We then minimize the asymmetric L(2) pseudo-distance between the surface of the deformable atlas and the surface topography range data. Once this registration is accomplished, the internal anatomy of the atlas is transformed to the coordinate system of the range data using elastic energy minimization. We evaluated our method by using it to register a digital mouse atlas to a surface model produced from a manually labeled CT mouse data set. Dice coefficents indicated excellent agreement in the brain and heart, with fair agreement in the kidneys and bladder. We also present example results produced using our method to align the digital mouse atlas to surface range data.

Gladiolus Rust Caused by Uromyces transversalis Makes First Nearctic Appearance in Florida.

The most serious rust pathogen of gladiolus (Gladiolus × hortulanus), Uromyces transversalis, has been listed as an exotic pathogen of concern for the United States for more than 80 years (4). Native to South Africa, the pathogen was reported in the Western Hemisphere for the first time in Brazil (2) and Argentina (1). Reports of gladiolus rust in several central Mexican states from 2004 to 2005 (3; http://www.pestalert.org/espanol/oprDetail.cfm?oprID=138 ) and interceptions at Mexican border stations and in Brazilian imports in 2005 at the port of Miami, FL collectively raised the alert level in the United States to high. In April 2006, the Hawaii Department of Agriculture notified the USDA of rust-infected gladiolus in a cut-flower shipment that was traced back to a 1,400-acre (565 ha) farm in Manatee County, FL. Inspection at the farm yielded samples that were quickly confirmed as U. transversalis by FDACS-DPI and USDA plant pathologists. The disease was identified in eight residential gardens near the commercial find and in another 700-acre (285 ha) farm in remote Hendry County, 100 miles to the southeast. In May 2006, gladiolus rust was detected in residential and commercial gladiolus in San Diego County, CA (see companion publication). On the advice of a USDA-assembled panel of experts, strict rust management guidelines and fallow host-free periods were implemented with the ultimate goal of eradication. Subsequent summer, fall, and now winter surveys in the infested commercial and residential areas have uncovered diminishing amounts of rust, with last traces detected on 9 September 2006. Commercial planting resumed at both farms in late summer, and crops remained rust free under weekly inspection until 15 February 2007 in Manatee County and 29 March 2007 in Hendry County. To insure a rust-free product, cut flowers are carefully inspected and foliage stripped at the packinghouse. Eradication will be attempted once more with a fallow host-free period before the 2007 season. U. transversalis is an autoecious rust that mainly infects Gladiolus spp., but has been known to infect other members of the Iridaceae: Anomatheca, Crocosmia, Melasphaerula, Tritonia, and Watsonia. Amphigenous uredinia form in transverse lines across gladiolus foliage and also on flower spikes under heavy disease pressure. The isolate present in Florida fits the literature description of U. transversalis in every respect (uredinia 0.5 to 1.5 mm in diameter, subglobose to ellipsoid verruculose yellow-amber urediniospores, 15 to 28 × 14 to 20 µm with wall 1.5 to 2.5 µm thick; telia also amphigenous, 0.5 to 1.3 µm in diameter, dark brown-black, subglobose to pyriform smooth amber teliospores, 20 to 30 × 15 to 20 µm with wall 1.5 to 2.0 µm thick, 4 to 6 µm thick at apex, pale brown to hyaline pedicel 30 to 40 µm long, yellow-brown paraphyses in pustule) ( http://nt.ars-grin.gov/fungaldatabases/new_allView.cfm?whichone=all&thisName=Uro myces%20transversalis&organismtype=Fungus ). Urediniospores initiated typical foliar lesions on transplanted gladiolus samples kept in the FDACS-DPI quarantine greenhouse during the diagnostic process. References: (1) J. R. Hernandez and J. F. Hennen. Sida 20:313, 2002. (2) G. P. B. Pitta et al. Biologica 47:323, 1981. (3) G. Rodriguez-Alvarado et al. Plant Dis. 90:687, 2006. (4) J. A. Stevenson. Page 82 in: Foreign Plant Diseases. USDA Fed. Hortic. Board Bureau Plant Ind. Government Printing Office, Washington DC, 1926.

Hidden Markov modelling of spike propagation from interictal MEG data.

For patients with partial epilepsy, automatic spike detection techniques applied to interictal MEG data often discover several potentially epileptogenic brain regions. An important determination in treatment planning is which of these detected regions are most likely to be the primary sources of epileptogenic activity. Analysis of the patterns of propagation activity between the detected regions may allow for detection of these primary epileptic foci. We describe the use of hidden Markov models (HMM) for estimation of the propagation patterns between several spiking regions from interictal MEG data. Analysis of the estimated transition probability matrix allows us to make inferences regarding the propagation pattern of the abnormal activity and determine the most likely region of its origin. The proposed HMM paradigm allows for a simple incorporation of the spike detector specificity and sensitivity characteristics. We develop bounds on performance for the case of perfect detection. We also apply the technique to simulated data sets in order to study the robustness of the method to the non-ideal specificity-sensitivity characteristics of the event detectors and compare results with the lower bounds. Our study demonstrates robustness of the proposed technique to event detection errors. We conclude with an example of the application of this method to a single patient.

Investigations of dipole localization accuracy in MEG using the bootstrap.

We describe the use of the nonparametric bootstrap to investigate the accuracy of current dipole localization from magnetoencephalography (MEG) studies of event-related neural activity. The bootstrap is well suited to the analysis of event-related MEG data since the experiments are repeated tens or even hundreds of times and averaged to achieve acceptable signal-to-noise ratios (SNRs). The set of repetitions or epochs can be viewed as a set of independent realizations of the brain's response to the experiment. Bootstrap resamples can be generated by sampling with replacement from these epochs and averaging. In this study, we applied the bootstrap resampling technique to MEG data from somatotopic experimental and simulated data. Four fingers of the right and left hand of a healthy subject were electrically stimulated, and about 400 trials per stimulation were recorded and averaged in order to measure the somatotopic mapping of the fingers in the S1 area of the brain. Based on single-trial recordings for each finger we performed 5000 bootstrap resamples. We reconstructed dipoles from these resampled averages using the Recursively Applied and Projected (RAP)-MUSIC source localization algorithm. We also performed a simulation for two dipolar sources with overlapping time courses embedded in realistic background brain activity generated using the prestimulus segments of the somatotopic data. To find correspondences between multiple sources in each bootstrap, sample dipoles with similar time series and forward fields were assumed to represent the same source. These dipoles were then clustered by a Gaussian Mixture Model (GMM) clustering algorithm using their combined normalized time series and topographies as feature vectors. The mean and standard deviation of the dipole position and the dipole time series in each cluster were computed to provide estimates of the accuracy of the reconstructed source locations and time series.

Mapping human brain function with MEG and EEG: methods and validation.

We survey the field of magnetoencephalography (MEG) and electroencephalography (EEG) source estimation. These modalities offer the potential for functional brain mapping with temporal resolution in the millisecond range. However, the limited number of spatial measurements and the ill-posedness of the inverse problem present significant limits to our ability to produce accurate spatial maps from these data without imposing major restrictions on the form of the inverse solution. Here we describe approaches to solving the forward problem of computing the mapping from putative inverse solutions into the data space. We then describe the inverse problem in terms of low dimensional solutions, based on the equivalent current dipole (ECD), and high dimensional solutions, in which images of neural activation are constrained to the cerebral cortex. We also address the issue of objective assessment of the relative performance of inverse procedures by the free-response receiver operating characteristic (FROC) curve. We conclude with a discussion of methods for assessing statistical significance of experimental results through use of the bootstrap for determining confidence regions in dipole-fitting methods, and random field (RF) and permutation methods for detecting significant activation in cortically constrained imaging studies.

Localization of realistic cortical activity in MEG using current multipoles.

We present a novel approach to MEG source estimation based on a regularized first-order multipole solution. The Gaussian regularizing prior is obtained by calculation of the sample mean and covariance matrix for the equivalent moments of realistic simulated cortical activity. We compare the regularized multipole localization framework to the classical dipole and general multipole source estimation methods by evaluating the ability of all three solutions to localize the centroids of physiologically plausible patches of activity simulated on the surface of a human cerebral cortex. The results, obtained with a realistic sensor configuration, a spherical head model, and given in terms of field and localization error, depict the performance of the dipolar and multipolar models as a function of variable source surface area (50-500 mm(2)), noise conditions (20, 10, and 5 dB SNR), source orientation (0-90 degrees ), and source depth (3-11 cm). We show that as the sources increase in size, they become less accurately modeled as current dipoles. The regularized multipole systematically outperforms the single dipole model, increasingly so as the spatial extent of the sources increases. In addition, our simulations demonstrate that as the orientation of the sources becomes more radial, dipole localization accuracy decreases substantially, while the performance of the regularized multipole model is far less sensitive to orientation and even succeeds in localizing quasi-radial source configurations. Furthermore, our results show that the multipole model is able to localize superficial sources with higher accuracy than the current dipole. These results indicate that the regularized multipole solution may be an attractive alternative to current-dipole-based source estimation methods in MEG.

Automated interictal spike detection and source localization in magnetoencephalography using independent components analysis and spatio-temporal clustering.

OBJECTIVE: Magnetoencephalography (MEG) dipole localization of epileptic spikes is useful in epilepsy surgery for mapping the extent of abnormal cortex and to focus intracranial electrodes. Visually analyzing large amounts of data produces fatigue and error. Most automated techniques are based on matching of interictal spike templates or predictive filtering of the data and do not explicitly include source localization as part of the analysis. This leads to poor sensitivity versus specificity characteristics. We describe a fully automated method that combines time-series analysis with source localization to detect clusters of focal neuronal current generators within the brain that produce interictal spike activity. METHODS: We first use an ICA (independent components analysis) method to decompose the multichannel MEG data and identify those components that exhibit spike-like characteristics. From these detected spikes we then find those whose spatial topographies across the array are consistent with focal neural sources, and determine the foci of equivalent current dipoles and their associated time courses. We then perform a clustering of the localized dipoles based on distance metrics that takes into consideration both their locations and time courses. The final step of refinement consists of retaining only those clusters that are statistically significant. The average locations and time series from significant clusters comprise the final output of our method. RESULTS AND SIGNIFICANCE: Data were processed from 4 patients with partial focal epilepsy. In all three subjects for whom surgical resection was performed, clusters were found in the vicinity of the resectioned area. CONCLUSIONS: The presented procedure is promising and likely to be useful to the physician as a more sensitive, automated and objective method to help in the localization of the interictal spike zone of intractable partial seizures. The final output can be visually verified by neurologists in terms of both the location and distribution of the dipole clusters and their associated time series. Due to the clinical relevance and demonstrated promise of this method, further investigation of this approach is warranted.

Model-based normalization for iterative 3D PET image reconstruction.

We describe a method for normalization in 3D PET for use with maximum a posteriori (MAP) or other iterative model-based image reconstruction methods. This approach is an extension of previous factored normalization methods in which we include separate factors for detector sensitivity, geometric response, block effects and deadtime. Since our MAP reconstruction approach already models some of the geometric factors in the forward projection, the normalization factors must be modified to account only for effects not already included in the model. We describe a maximum likelihood approach to joint estimation of the count-rate independent normalization factors, which we apply to data from a uniform cylindrical source. We then compute block-wise and block-profile deadtime correction factors using singles and coincidence data, respectively, from a multiframe cylindrical source. We have applied this method for reconstruction of data from the Concorde microPET P4 scanner. Quantitative evaluation of this method using well-counter measurements of activity in a multicompartment phantom compares favourably with normalization based directly on cylindrical source measurements.

Internet2-based 3D PET image reconstruction using a PC cluster.

We describe an approach to fast iterative reconstruction from fully three-dimensional (3D) PET data using a network of PentiumIII PCs configured as a Beowulf cluster. To facilitate the use of this system, we have developed a browser-based interface using Java. The system compresses PET data on the user's machine, sends these data over a network, and instructs the PC cluster to reconstruct the image. The cluster implements a parallelized version of our preconditioned conjugate gradient method for fully 3D MAP image reconstruction. We report on the speed-up factors using the Beowulf approach and the impacts of communication latencies in the local cluster network and the network connection between the user's machine and our PC cluster.

On MEG forward modelling using multipolar expansions.

Magnetoencephalography (MEG) is a non-invasive functional imaging modality based on the measurement of the external magnetic field produced by neural current sources within the brain. The reconstruction of the underlying sources is a severely ill-posed inverse problem typically tackled using either low-dimensional parametric source models, such as an equivalent current dipole (ECD), or high-dimensional minimum-norm imaging techniques. The inability of the ECD to properly represent non-focal sources and the over-smoothed solutions obtained by minimum-norm methods underline the need for an alternative approach. Multipole expansion methods have the advantages of the parametric approach while at the same time adequately describing sources with significant spatial extent and arbitrary activation patterns. In this paper we first present a comparative review of spherical harmonic and Cartesian multipole expansion methods that can be used in MEG. The equations are given for the general case of arbitrary conductors and realistic sensor configurations and also for the special cases of spherically symmetric conductors and radially oriented sensors. We then report the results of computer simulations used to investigate the ability of a first-order multipole model (dipole and quadrupole) to represent spatially extended sources, which are simulated by 2D and 3D clusters of elemental dipoles. The overall field of a cluster is analysed using singular value decomposition and compared to the unit fields of a multipole, centred in the middle of the cluster, using subspace correlation metrics. Our results demonstrate the superior utility of the multipolar source model over ECD models in providing source representations of extended regions of activity.

First Report of Dieback Caused by Verticillium dahliae on Blighia sapida in the United States.

Mature akee trees, Blighia sapida K. Koenig, in a local south Florida commercial orchard had wilt and dieback symptoms during spring 1999. A fungus isolated from the gray xylem root tissue on V8 agar was identified as Verticillium dahliae Klebahn at the Division of Plant Industry of the Florida Department of Agriculture and Consumer Services. Twenty akee seedlings were transplanted into 3.85-liter plastic pots and grown in a greenhouse at a daytime temperature of 28°C and nighttime temperature of 23°C. When plants were approximately 25 cm high, a 15-cm knife was used to sever roots in the four quadrants of each pot. Inoculum was made from a 2-week-old culture of V. dahliae on V8 agar and blended with 160 ml of sterile water, and 15 ml of this slurry was poured into the disturbed soil of each of 10 treated plants. A plate of uninoculated V8 agar was applied, as above, to 10 control plants. Plants were kept in the greenhouse. After 6 weeks, inoculated plants showed symptoms of leaf wilt, dieback and plant death. No symptoms were seen on control plants. V. dahliae was isolated directly from the gray vascular tissue of inoculated plants. The inoculation experiment was repeated three times, fulfilling Koch's postulates. To our knowledge, this is the first report of Verticillium dieback on B. sapida in the United States.

Automated graph-based analysis and correction of cortical volume topology.

The human cerebral cortex is topologically equivalent to a sheet and can be considered topologically spherical if it is closed at the brain stem. Low-level segmentation of magnetic resonance (MR) imagery typically produces cerebral volumes whose tessellations are not topologically spherical. We present a novel algorithm that analyzes and constrains the topology of a volumetric object. Graphs are formed that represent the connectivity of voxel segments in the foreground and background of the image. These graphs are analyzed and minimal corrections to the volume are made prior to tessellation. We apply the algorithm to a simple test object and to cerebral white matter masks generated by a low-level tissue identification sequence. We tessellate the resulting objects using the marching cubes algorithm and verify their topology by computing their Euler characteristics. A key benefit of the algorithm is that it localizes the change to a volume to the specific areas of its topological defects.

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.

Resolution and noise properties of MAP reconstruction for fully 3-D PET.

We derive approximate analytical expressions for the local impulse response and covariance of images reconstructed from fully three-dimensional (3-D) positron emission tomography (PET) data using maximum a posteriori (MAP) estimation. These expressions explicitly account for the spatially variant detector response and sensitivity of a 3-D tomograph. The resulting spatially variant impulse response and covariance are computed using 3-D Fourier transforms. A truncated Gaussian distribution is used to account for the effect on the variance of the nonnegativity constraint used in MAP reconstruction. Using Monte Carlo simulations and phantom data from the microPET small animal scanner, we show that the approximations provide reasonably accurate estimates of contrast recovery and covariance of MAP reconstruction for priors with quadratic energy functions. We also describe how these analytical results can be used to achieve near-uniform contrast recovery throughout the reconstructed volume.