This paper introduces and analyzes a linear minimum mean square error (LMMSE) estimator using a Rician noise model and its recursive version (RLMMSE) for the restoration of diffusion weighted images. A method to estimate the noise level based on local estimations of mean or variance is used to automatically parametrize the estimator. The restoration performance is evaluated using quality indexes and compared to alternative estimation schemes. The overall scheme is simple, robust, fast, and improves estimations. Filtering diffusion weighted magnetic resonance imaging (DW-MRI) with the proposed methodology leads to more accurate tensor estimations. Real and synthetic datasets are analyzed.
A new method for noise filtering in images that follow a Rician model-with particular attention to magnetic resonance imaging-is proposed. To that end, we have derived a (novel) closed-form solution of the linear minimum mean square error (LMMSE) estimator for this distribution. Additionally, a set of methods that automatically estimate the noise power are developed. These methods use information of the sample distribution of local statistics of the image, such as the local variance, the local mean, and the local mean square value. Accordingly, the dynamic estimation of noise leads to a recursive version of the LMMSE, which shows a good performance in both noise cleaning and feature preservation. This paper also includes the derivation of the probability density function of several local sample statistics for the Rayleigh and Rician model, upon which the estimators are built.
This paper presents a segmentation method that extends geodesic active region methods by the incorporation of a statistical classifier trained using feature selection. The classifier provides class probability maps based on class representative local features, and the geodesic active region formulation enables the partitioning of the image according to the region information. We demonstrate automatic segmentation results of the myocardium in cardiac late gadolinium-enhanced magnetic resonance imaging (CE-MRI) data using coupled level set curve evolutions, in which the classifier is incorporated both from a region term and from a shape term from particle filtering. The results show potential for clinical studies of scar tissue in late CE-MRI data.
We introduce a fibre tract segmentation algorithm based on the geometric coherence of fibre orientations as indicated by a streamline flow model. The inference of local flow approximations motivates a pairwise consistency measure between fibre ODF maxima. We use this measure in a recursive algorithm to cluster consistent ODF maxima, leading to the segmentation of white matter pathways. The method requires minimal seeding compared to streamline tractography-based methods, and allows multiple tracts to pass through the same voxels. We illustrate the approach with a segmentation of the corpus callosum and one of the cortico-spinal tract, with each example seeded at a single voxel.
OBJECTIVE: Impairment of white matter connecting frontal and temporal cortices has been reported in schizophrenia. Yet, not much is known about the effects of age on fibers connecting these brain regions. Using diffusion tensor imaging tractography, we investigated the relationship between age and fiber integrity in patients with schizophrenia vs. healthy adults. METHODS: DTI tractography was used to create 3D reconstructions of the cingulum, uncinate and inferior occipito-frontal fasciculi in 27 patients with schizophrenia and 34 healthy volunteers (23-56 years of age, group-matched on age). Fractional anisotropy (FA), describing fiber integrity, was then calculated along the entire length of these tracts, and correlated with subjects’ age. RESULTS: Patients revealed a significant decline in FA with age in both the cingulum and uncinate, but not in the inferior occipito-frontal fasciculi. No statistically significant correlations were found in these fiber bundles in controls. CONCLUSIONS: These results suggest an age-associated reduction of frontal-temporal connectivity in schizophrenia, but not in healthy controls.
The advancement of the computational biology field hinges on progress in three fundamental directions—the development of new computational algorithms, the availability of informatics resource management infrastructures and the capability of tools to interoperate and synergize. There is an explosion in algorithms and tools for computational biology, which makes it difficult for biologists to find, compare and integrate such resources. We describe a new infrastructure, iTools, for managing the query, traversal and comparison of diverse computational biology resources. Specifically, iTools stores information about three types of resources—data, software tools and web-services. The iTools design, implementation and resource meta-data content reflect the broad research, computational, applied and scientific expertise available at the seven National Centers for Biomedical Computing. iTools provides a system for classification, categorization and integration of different computational biology resources across space-and-time scales, biomedical problems, computational infrastructures and mathematical foundations. A large number of resources are already iTools-accessible to the community and this infrastructure is rapidly growing. iTools includes human and machine interfaces to its resource meta-data repository. Investigators or computer programs may utilize these interfaces to search, compare, expand, revise and mine meta-data descriptions of existent computational biology resources. We propose two ways to browse and display the iTools dynamic collection of resources. The first one is based on an ontology of computational biology resources, and the second one is derived from hyperbolic projections of manifolds or complex structures onto planar discs. iTools is an open source project both in terms of the source code development as well as its meta-data content. iTools employs a decentralized, portable, scalable and lightweight framework for long-term resource management. We demonstrate several applications of iTools as a framework for integrated bioinformatics. iTools and the complete details about its specifications, usage and interfaces are available at the iTools web page http://iTools.ccb.ucla.edu.
Whereas high angular resolution reconstruction methods for diffusion MRI can estimate multiple dominant fibre orientations within a single imaging voxel, they are fundamentally limited in certain cases of complex subvoxel fibre structures, resulting in ambiguous local orientation distribution functions. In this article we address the important problem of disambiguating such complex subvoxel fibre tract configurations, with the purpose of improving the performance of fibre tractography. We do so by extending a curve inference method to distinguish between the cases of curving and fanning fibre bundles using differential geometric estimates in a local neighbourhood. The key benefit of this method is the inference of curves, instead of only fibre orientations, to model the underlying fibre bundles. This in turn allows distinct fibre geometries that contain nearly identical sets of fibre orientations at a voxel, to be distinguished from one another. Experimental results demonstrate the ability of the method to successfully label voxels into one of the above categories and improve the performance of a fibre-tracking algorithm.
OBJECTIVE: To evaluate volumes and asymmetry of superior temporal gyrus (STG) and correlate these measures with a neurocognitive evaluation of verbal performance in Williams syndrome (WS) and in a typically developing age-matched and sex-matched group. BACKGROUND: Despite initial claims of language strength in WS, recent studies suggest delayed language milestones. The STG is implicated in linguistic processing and is a highly lateralized brain region. METHOD: Here, we examined STG volumes and asymmetry of STG in WS patients and in age-matched controls. We also correlated volume of STG with a subset of verbal measures. Magnetic resonance imaging scans were obtained on a GE 1.5-T magnet with 1.5-mm contiguous slices, and were used to measure whole gray matter, white matter, and cerebrospinal fluid volumes, and also STG volume. RESULTS: Results revealed significantly reduced intracranial volume in WS patients, compared with controls. Right and left STG were also significantly smaller in WS patients. In addition, compared with normal controls, a lack of normal left >right STG asymmetry was evident in WS. Also of note was the finding that, in contrast to controls, WS patients did not reveal a positive correlation between verbal intelligence quotient and left STG volume, which further suggests a disruption in this region of the brain. CONCLUSIONS: In conclusion, atypical patterns of asymmetry and reduced STG volume in WS were observed, which may, in part, contribute to some of the linguistic impairments found in this cohort of WS patients.
A lyotropic nonionic lamellar system composed of pentaethyleneglycol mono n-dodecyl ether and D(2)O was studied using natural abundance (13)C NMR under magic-angle spinning. Applying a two-dimensional recoupling method proposed by Dvinskikh (R-PDLF), (1)H-(13)C dipolar couplings were estimated over a range of temperatures (300-335 K), thus enabling analysis of structural changes in the liquid crystalline system. The results obtained are used to correlate the conformation and mobility of local sites in the surfactant molecule with overall changes in the lamellar structure.
BACKGROUND: In chronic schizophrenia and chronic bipolar disorder, gamma band (30-100 Hz) auditory steady-state electroencephalogram responses (ASSRs) are reduced in power and phase locking, likely reflecting neural circuit dysfunction. Here we examined whether gamma ASSR deficits are also present at first hospitalization for psychosis. METHODS: Subjects were 16 first episode schizophrenia patients (SZ), 16 first episode affective disorder patients (AFF) (13 with bipolar disorder), and 33 healthy control subjects (HC). Stimuli were 20-, 30-, and 40-Hz binaural click trains. The ASSR phase locking and evoked power were analyzed with the Morlet wavelet transform. RESULTS: At 40-Hz stimulation, SZ and AFF had significantly reduced phase locking compared with HC. This deficit was more pronounced over the left hemisphere in SZ. Evoked power at 40 Hz was also reduced in the patients compared with HC. At 30-Hz stimulation phase locking and evoked power were reduced in both patient groups. The 20-Hz ASSR did not differ between groups, but phase locking and evoked power of the 40-Hz harmonic of the 20-Hz ASSR were reduced in both SZ and AFF. Phase locking of this 40-Hz harmonic was correlated with total positive symptoms in SZ. CONCLUSIONS: The gamma ASSR deficit is present at first hospitalization for both schizophrenia and affective disorder but shows a left hemisphere bias in first hospitalized SZ. Some of the neural circuitry abnormalities underlying the gamma ASSR deficit might be common to psychoses in general, whereas others might be specific to particular disorders.
