PURPOSE: To prospectively investigate the factors--including subject, brain hemisphere, study site, field strength, imaging unit vendor, imaging run, and examination visit--affecting the reproducibility of functional magnetic resonance (MR) imaging activations based on a repeated sensory-motor (SM) task.
MATERIALS AND METHODS: The institutional review boards of all participating sites approved this HIPAA-compliant study. All subjects gave informed consent. Functional MR imaging data were repeatedly acquired from five healthy men aged 20-29 years who performed the same SM task at 10 sites. Five 1.5-T MR imaging units, four 3.0-T units, and one 4.0-T unit were used. The subjects performed bilateral finger tapping on button boxes with a 3-Hz audio cue and a reversing checkerboard. In a block design, 15-second epochs of alternating baseline and tasks yielded 85 acquisitions per run. Functional MR images were acquired with block-design echo-planar or spiral gradient-echo sequences. Brain activation maps standardized in a unit-sphere for the left and right hemispheres of each subject were constructed. Areas under the receiver operating characteristic curve, intraclass correlation coefficients, multiple regression analysis, and paired Student t tests were used for statistical analyses.
RESULTS: Significant factors were subject (P < .005), k-space (P < .005), and field strength (P = .02) for sensitivity and subject (P = .03) and k-space (P = .05) for specificity. At 1.5-T MR imaging, mean sensitivities ranged from 7% to 32% and mean specificities were higher than 99%. At 3.0 T, mean sensitivities and specificities ranged from 42% to 85% and from 96% to 99%, respectively. At 4.0 T, mean sensitivities and specificities ranged from 41% to 73% and from 95% to 99%, respectively. Mean areas under the receiver operating characteristic curve (+/- their standard errors) were 0.77 +/- 0.05 at 1.5 T, 0.90 +/- 0.09 at 3.0 T, and 0.95 +/- 0.02 at 4.0 T, with significant differences between the 1.5- and 3.0-T examinations and between the 1.5- and 4.0-T examinations (P < .01 for both comparisons). Intraclass correlation coefficients ranged from 0.49 to 0.71.
CONCLUSION: MR imaging at 3.0- and 4.0-T yielded higher reproducibility across sites and significantly better results than 1.5-T imaging. The effects of subject, k-space, and field strength on examination reproducibility were significant.
We present a new algorithm to register 3-D preoperative magnetic resonance (MR) images to intraoperative MR images of the brain which have undergone brain shift. This algorithm relies on a robust estimation of the deformation from a sparse noisy set of measured displacements. We propose a new framework to compute the displacement field in an iterative process, allowing the solution to gradually move from an approximation formulation (minimizing the sum of a regularization term and a data error term) to an interpolation formulation (least square minimization of the data error term). An outlier rejection step is introduced in this gradual registration process using a weighted least trimmed squares approach, aiming at improving the robustness of the algorithm. We use a patient-specific model discretized with the finite element method in order to ensure a realistic mechanical behavior of the brain tissue. To meet the clinical time constraint, we parallelized the slowest step of the algorithm so that we can perform a full 3-D image registration in 35 s (including the image update time) on a heterogeneous cluster of 15 personal computers. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift of up to 14 mm. The results show a good ability to recover large displacements, and a limited decrease of accuracy near the tumor resection cavity.
BACKGROUND AND PURPOSE: Talairach-based parcellation (TP) of human brain magnetic resonance imaging (MRI) data has been used increasingly in clinical research to make regional measurements of brain structures in vivo. Recently, TP has been applied to pediatric research to elucidate the changes in regional brain volumes related to several neurological disorders. However, all freely available tools have been designed to parcellate adult brain MRI data. Parcellation of neonatal MRI data is very challenging owing to the lack of strong signal contrast, variability in signal intensity within tissues, and the small size and thus difficulty in identifying small structures used as landmarks for TP. Hence the authors designed and validated a new interactive tool to parcellate brain MRI data from newborns and young infants.
METHODS: The authors' tool was developed as part of a postprocessing pipeline, which includes registration of multichannel MR images, segmentation, and parcellation of the segmented data. The tool employs user-friendly interactive software to visualize and assign the anatomic landmarks required for parcellation, after which the planes and parcels are generated automatically by the algorithm. The authors then performed 3 sets of validation experiments to test the precision and reliability of their tool.
RESULTS: Validation experiments of intra-and interrater reliability on data obtained from newborn and 1-year-old children showed a very high sensitivity of >95% and specificity >99.9%. The authors also showed that rotating and reformatting the original MRI data results in a statistically significant difference in parcel volumes, demonstrating the importance of using a tool such as theirs that does not require realignment of the data prior to parcellation.
CONCLUSIONS: To the authors' knowledge, the presented approach is the first TP method that has been developed and validated specifically for neonatal brain MRI data. Their approach would also be valuable for the analysis of brain MRI data from older children and adults.
Segmentation of ultrasound images is necessary in a variety of clinical applications, but the development of automatic techniques is still an open problem. Spectral clustering techniques have recently become popular for data and image analysis. In particular, image segmentation has been proposed via the normalized cut (NCut) criterion. This article describes an initial investigation to determine the suitability of such segmentation techniques for ultrasound images. The adaptation of the NCut technique to ultrasound is described first. Segmentation is then performed on simulated ultrasound images. Tests are also performed on abdominal and fetal images with the segmentation results compared to manual segmentation. The success of the segmentation on these test cases warrants further research into NCut-based segmentation of ultrasound images.
A new framework is presented for clustering fiber tracts into anatomically known bundles. This work is motivated by medical applications in which variation analysis of known bundles of fiber tracts in the human brain is desired. To include the anatomical knowledge in the clustering, we invoke an atlas of fiber tracts, labeled by the number of bundles of interest. In this work, we construct such an atlas and use it to cluster all fiber tracts in the white matter. To build the atlas, we start with a set of labeled ROIs specified by an expert and extract the fiber tracts initiating from each ROI. Affine registration is used to project the extracted fiber tracts of each subject to the atlas, whereas their B-spline representation is used to efficiently compare them to the fiber tracts in the atlas and assign cluster labels. Expert visual inspection of the result confirms that the proposed method is very promising and efficient in clustering of the known bundles of fiber tracts.
This paper describes a new framework for white matter tractography in high angular resolution diffusion data. A direction-dependent local cost is defined based on the diffusion data for every direction on the unit sphere. Minimum cost curves are determined by solving the Hamilton-Jacobi-Bellman using an efficient algorithm. Classical costs based on the diffusion tensor field can be seen as a special case. While the minimum cost (or equivalently the travel time of a particle moving along the curve) and the anisotropic front propagation frameworks are related, front speed is related to particle speed through a Legendre transformation which can severely impact anisotropy information for front propagation techniques. Implementation details and results on high angular diffusion data show that this method can successfully take advantage of the increased angular resolution in high b-value diffusion weighted data despite lower signal to noise ratio.
This paper presents a novel approach that three-dimensionally visualizes and evaluates stenoses in human coronary arteries by using harmonic skeletons. A harmonic skeleton is the center line of a multi-branched tubular surface extracted based on a harmonic function, which is the solution of the Laplace equation. This skeletonization method guarantees smoothness and connectivity and provides a fast and straightforward way to calculate local cross-sectional areas of the arteries, and thus provides the possibility to localize and evaluate coronary artery stenosis, which is a commonly seen pathology in coronary artery disease.
We present a new algorithm to register 3D pre-operative Magnetic Resonance (MR) images with intra-operative MR images of the brain. This algorithm relies on a robust estimation of the deformation from a sparse set of measured displacements. We propose a new framework to compute iteratively the displacement field starting from an approximation formulation (minimizing the sum of a regularization term and a data error term) and converging toward an interpolation formulation (least square minimization of the data error term). The robustness of the algorithm is achieved through the introduction of an outliers rejection step in this gradual registration process. We ensure the validity of the deformation by the use of a biomechanical model of the brain specific to the patient, discretized with the finite element method. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift up to 13 mm.
This paper presents a new algorithm for non-rigid registration between two doubly-connected regions. Our algorithm is based on harmonic analysis and the theory of optimal mass transport. It assumes an underlining continuum model, in which the total amount of mass is exactly preserved during the transformation of tissues. We use a finite element approach to numerically implement the algorithm.
Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data.
In this work we address the uncertainty associated with fiber paths obtained in white matter fiber tractography. This uncertainty, which arises for example from noise and partial volume effects, is quantified using a Bayesian modeling framework. The theory for estimating the probability of a connection between two areas in the brain is presented, and a new model of the local water diffusion profile is introduced. We also provide a theorem that facilitates the estimation of the parameters in this diffusion model, making the presented method simple to implement.
We present a statistical framework that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image inhomogeneities, anatomical labelmap, and a mapping from the atlas to the image space. An example of the approach is given for a brain structure-dependent affine mapping approach. The algorithm produces high quality segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. In addition, we show that the approach performs better than similar methods which separate the registration and segmentation problems.
We present a novel method for finding white matter fiber correspondences and clusters across a population of brains. Our input is a collection of paths from tractography in every brain. Using spectral methods we embed each path as a vector in a high dimensional space. We create the embedding space so that it is common across all brains, consequently similar paths in all brains will map to points near each other in the space. By performing clustering in this space we are able to find matching fiber tract clusters in all brains. In addition, we automatically obtain correspondence of tractographic paths across brains: by selecting one or several paths of interest in one brain, the most similar paths in all brains are obtained as the nearest points in the high-dimensional space.
In the present study, fully nonlinear (i.e. accounting for both geometric and material nonlinearities) patient specific finite element brain model was applied to predict deformation field within the brain during the craniotomy-induced brain shift. Deformation of brain surface was used as displacement boundary conditions. Application of the computed deformation field to align (i.e. register) the preoperative images with the intraoperative ones indicated that the model very accurately predicts the displacements of gravity centers of the lateral ventricles and tumor even for very limited information about the brain surface deformation. These results are sufficient to suggest that nonlinear biomechanical models can be regarded as one possible way of complementing medical image processing techniques when conducting nonrigid registration. Important advantage of such models over the linear ones is that they do not require unrealistic assumptions that brain deformations are infinitesimally small and brain tissue stress-strain relationship is linear.
Image segmentation algorithms derived from spectral clustering analysis rely on the eigenvectors of the Laplacian of a weighted graph obtained from the image. The NCut criterion was previously used for image segmentation in supervised manner. We derive a new strategy for unsupervised image segmentation. This article describes an initial investigation to determine the suitability of such segmentation techniques for ultrasound images. The extension of the NCut technique to the unsupervised clustering is first described. The novel segmentation algorithm is then performed on simulated ultrasound images. Tests are also performed on abdominal and fetal images with the segmentation results compared to manual segmentation. Comparisons with the classical NCut algorithm are also presented. Finally, segmentation results on other types of medical images are shown.
A hierarchical model based on the Multivariate Autoregessive (MAR) process is proposed to jointly model neurological time-series collected from multiple subjects, and to characterize the distribution of MAR coefficients across the population from which those subjects were drawn. Thus, inference about effective connectivity between brain regions may be generalized beyond those subjects studied. The posterior on population- and subject-level connectivity parameters are estimated in a Variational Bayesian (VB) framework, and structural model parameters are chosen by the corresponding evidence criteria. The significance of resulting connectivity statistics are evaluated by permutation-based approximations to the null distribution. The method is demonstrated on simulated data and on actual multi-subject neurological time-series.
BACKGROUND: Findings from postmortem studies suggest reduced prefrontal cortical thickness in schizophrenia; however, cortical thickness in first-episode schizophrenia has not been evaluated using magnetic resonance imaging (MRI).
METHODS: Prefrontal cortical thickness was measured using MRI in first-episode schizophrenia patients (n = 17), first-episode affective psychosis patients (n = 17), and normal control subjects (n = 17); subjects were age-matched within 2 years and within a narrow age range (18-29 years). A previous study using the same subjects reported reduced prefrontal gray matter volume in first-episode schizophrenia. Manual editing was performed on those prefrontal segmentations before cortical thickness was measured.
RESULTS: Prefrontal cortical thickness was not significantly different among groups. Prefrontal gray matter volume and thickness were, however, positively correlated in both schizophrenia and control subjects. The product of boundary complexity and thickness, an alternative measure of volume, was positively correlated with volume for all three groups. Finally, age and age at first medication were negatively correlated with prefrontal cortical thickness only in first-episode schizophrenia.
CONCLUSIONS: This study demonstrates the potential usefulness of MRI for the study of cortical thickness abnormalities in schizophrenia. Correlations between cortical thickness and age and between cortical thickness and age at first medication suggest that the longer the schizophrenic process has been operative, the thinner the prefrontal cortex, although this needs confirmation in a longitudinal study.
RATIONALE AND OBJECTIVES: Both single-shot diffusion-weighted echo-planar imaging (EPI) and line scan diffusion imaging (LSDI) can be used to obtain magnetic resonance diffusion tensor data and to calculate directionally invariant diffusion anisotropy indices, ie, indirect measures of the organization and coherence of white matter fibers in the brain. To date, there has been no comparison of EPI and LSDI. Because EPI is the most commonly used technique for acquiring diffusion tensor data, it is important to understand the limitations and advantages of LSDI relative to EPI.
MATERIALS AND METHODS: Five healthy volunteers underwent EPI and LSDI diffusion on a 1.5 Tesla magnet (General Electric Medical Systems, Milwaukee, WI). Four-mm thick coronal sections, covering the entire brain, were obtained. In addition, one subject was tested with both sequences over four sessions. For each image voxel, eigenvectors and eigenvalues of the diffusion tensor were calculated, and fractional anisotropy (FA) was derived. Several regions of interest were delineated, and for each, mean FA and estimated mean standard deviation were calculated and compared.
RESULTS: Results showed no significant differences between EPI and LSDI for mean FA for the five subjects. When intersession reproducibility for one subject was evaluated, there was a significant difference between EPI and LSDI in FA for the corpus callosum and the right uncinate fasciculus. Moreover, errors associated with each FA measure were larger for EPI than for LSDI.
CONCLUSION: Results indicate that both EPI- and LSDI-derived FA measures are sufficiently robust. However, when higher accuracy is needed, LSDI provides smaller error and smaller inter-subject and inter-session variability than EPI.
Placental insufficiency with fetal intrauterine growth restriction (IUGR) is an important cause of perinatal mortality and morbidity and is subsequently associated with significant neurodevelopmental impairment in cognitive function, attention capacity, and school performance. The underlying biologic cause for this association is unclear. Twenty-eight preterm infants (gestational age 32.5 +/- 1.9 wk) were studied by early and term magnetic resonance imaging (MRI). An advanced quantitative volumetric three-dimensional MRI technique was used to measure brain tissue volumes in 14 premature infants with placental insufficiency, defined by abnormal antenatal Doppler measurements and mean birth weights <10(th) percentile (1246 +/- 299 g) (IUGR) and in 14 preterm infants matched for gestational age with normal mean birth weights 1843 +/- 246 g (control). Functional outcome was measured at term in all infants by a specialized assessment scale of preterm infant behavior. Premature infants with IUGR had a significant reduction in intracranial volume (mean +/- SD: 253.7 +/- 29.9 versus 300.5 +/- 43.5 mL, p < 0.01) and in cerebral cortical gray matter (mean +/- SD: 77.2 +/- 16.3 versus 106.8 +/- 24.6 mL, p < 0.01) when measured within the first 2 wk of life compared with control premature infants. These findings persisted at term with intracranial volume (mean +/- SD: 429.3 +/- 47.9 versus 475.9 +/- 53.4 mL, p < 0.05) and cerebral cortical gray matter (mean +/- SD: 149.3 +/- 29.2 versus 189 +/- 34.2 mL, p < 0.01). Behavioral assessment at term showed a significantly less mature score in the subsystem of attention-interaction availability in IUGR infants (p < 0.01). Cerebral cortical gray matter volume at term correlated with attention-interaction capacity measured at term (r = 0.45, p < 0.05). These results suggest that placental insufficiency with IUGR have specific structural and functional consequences on cerebral cortical brain development. These findings may provide insight into the structural-functional correlate for the developmental deficits associated with IUGR.
OBJECTIVE: To investigate the effects of early experience on brain function and structure.
METHODS: A randomized clinical trial tested the neurodevelopmental effectiveness of the Newborn Individualized Developmental Care and Assessment Program (NIDCAP). Thirty preterm infants, 28 to 33 weeks' gestational age (GA) at birth and free of known developmental risk factors, participated in the trial. NIDCAP was initiated within 72 hours of intensive care unit admission and continued to the age of 2 weeks, corrected for prematurity. Control (14) and experimental (16) infants were assessed at 2 weeks' and 9 months' corrected age on health status, growth, and neurobehavior, and at 2 weeks' corrected age additionally on electroencephalogram spectral coherence, magnetic resonance diffusion tensor imaging, and measurements of transverse relaxation time.
RESULTS: The groups were medically and demographically comparable before as well as after the treatment. However, the experimental group showed significantly better neurobehavioral functioning, increased coherence between frontal and a broad spectrum of mainly occipital brain regions, and higher relative anisotropy in left internal capsule, with a trend for right internal capsule and frontal white matter. Transverse relaxation time showed no difference. Behavioral function was improved also at 9 months' corrected age. The relationship among the 3 neurodevelopmental domains was significant. The results indicated consistently better function and more mature fiber structure for experimental infants compared with their controls.
CONCLUSIONS: This is the first in vivo evidence of enhanced brain function and structure due to the NIDCAP. The study demonstrates that quality of experience before term may influence brain development significantly.