Both post-mortem and neuroimaging studies have contributed significantly to what we know about the brain and schizophrenia. MRI studies of volumetric reduction in several brain regions in schizophrenia have confirmed early speculations that the brain is disordered in schizophrenia. There is also a growing body of evidence suggesting that a disturbance in connectivity between different brain regions, rather than abnormalities within the separate regions themselves, are responsible for the clinical symptoms and cognitive dysfunctions observed in this disorder. Thus an interest in white matter fiber tracts, subserving anatomical connections between distant, as well as proximal, brain regions, is emerging. This interest coincides with the recent advent of diffusion tensor imaging (DTI), which makes it possible to evaluate the organization and coherence of white matter fiber tracts. This is an important advance as conventional MRI techniques are insensitive to fiber tract direction and organization, and have not consistently demonstrated white matter abnormalities. DTI may, therefore, provide important new information about neural circuitry, and it is increasingly being used in neuroimaging studies of psychopathological disorders. Of note, in the past five years 18 DTI studies in schizophrenia have been published, most describing white matter abnormalities. Questions still remain, however, regarding what we are measuring that is abnormal in this disease, and how measures obtained using one method correspond to those obtained using other methods? Below we review the basic principles involved in MR-DTI, followed by a review of the different methods used to evaluate diffusion. Finally, we review MR-DTI findings in schizophrenia.
This contribution presents finite element computation of the deformation field within the brain during craniotomy-induced brain shift. The results were used to illustrate the capabilities of non-linear (i.e. accounting for both geometric and material non-linearities) finite element analysis in non-rigid registration of pre- and intra-operative magnetic resonance images of the brain. We used patient-specific hexahedron-dominant finite element mesh, together with realistic material properties for the brain tissue and appropriate contact conditions at boundaries. The model was loaded by the enforced motion of nodes (i.e. through prescribed motion of a boundary) at the brain surface in the craniotomy area. We suggest using explicit time-integration scheme for discretised equations of motion, as the computational times are much shorter and accuracy, for practical purposes, the same as in the case of implicit integration schemes. Application of the computed deformation field to register (i.e. align) the pre-operative images with the intra-operative ones indicated that the model very accurately predicts the displacements of the tumour and the lateral ventricles even for limited information about the brain surface deformation. The prediction accuracy improves when information about deformation of not only exposed (during craniotomy) but also unexposed parts of the brain surface is used when prescribing loading. However, it appears that the accuracy achieved using information only about the deformation of the exposed surface, that can be determined without intra-operative imaging, is acceptable. The presented results show that non-linear biomechanical models can complement medical image processing techniques when conducting non-rigid registration. Important advantage of such models over the previously used linear ones is that they do not require unrealistic assumptions that brain deformations are infinitesimally small and brain stress-strain relationship is linear.
Many neuroanatomy studies rely on brain tissue segmentations of magnetic resonance images (MRI). We present a segmentation tool, which performs this task automatically by analyzing the MRIs as well as tissue specific spatial priors. The priors are aligned to the patient through a non-rigid registration method. The segmentation itself is parameterized by an XML file making the approach easily adjustable to various segmentation problems. The tool is hidden beneath a 'one-button' user interface, which is simple to install and is applicable to a wide variety of image acquisition protocols.
In this work, we extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment. Our results indicate that increasing the complexity of the deformation model improves registration accuracy significantly, especially at cortical regions.
We demonstrate an EM-based algorithm that jointly registers and clusters a group of images using an affine transformation model. The output is a small number of prototype images that represent the different modes of the population. The proposed algorithm can be viewed as a generalization of other well-known atlas construction algorithms, where the collection of prototypes represent multiple atlases for that population. Our experiments indicate that the employment of multiple atlases improves the localization of the underlying structure in a new subject.
Neuroanatomical structure appears to be altered in preterm infants, but there has been little insight into the major perinatal risk factors associated with regional cerebral structural alterations. MR images were taken to quantitatively compare regional brain tissue volumes between term and preterm infants and to investigate associations between perinatal risk factors and regional neuroanatomical alterations in a large cohort of preterm infants. In a large prospective longitudinal cohort study of 202 preterm and 36 term infants, MR scans at term equivalent were undertaken for volumetric estimates of cortical and deep nuclear grey matter, unmyelinated and myelinated white matter (WM) and CSF within 8 parcellated regions for each hemisphere of the brain. Perinatal correlates analysed in relation to regional brain structure included gender, gestational age, intrauterine growth restriction, bronchopulmonary dysplasia, white matter injury (WMI) and intraventricular haemorrhage. Results revealed region-specific reductions in brain volumes in preterm infants compared with term controls in the parieto-occipital (preterm mean difference: -8.1%; 95% CI = -13.8--2.3%), sensorimotor (-11.6%; -18.2--5.0%), orbitofrontal (-30.6%; -49.8--11.3%) and premotor (-7.6%; -14.2--0.9%) regions. Within the sensorimotor and orbitofrontal regions cortical grey matter and unmyelinated WM were most clearly reduced in preterm infants, whereas deep nuclear grey matter was reduced mainly within the parieto-occipital and subgenual regions. CSF (ventricular and extracerebral) was doubled in volume within the superior regions in preterm infants compared with term controls. Cerebral WMI and intrauterine growth restriction were both associated with a more posterior reduction in brain volumes, whereas bronchopulmonary dysplasia was associated with a more global reduction across all regions. In contrast degree of immaturity was not related to regional brain structure among preterm infants. In summary, preterm birth is associated with regional cerebral tissue reductions, with the adverse pattern varying between risk factors. These findings add to our understanding of the potential pathways leading to altered brain structure and outcome in the preterm infant.
Clustering is often formulated as the maximum likelihood estimation of a mixture model that explains the data. The EM algorithm widely used to solve the resulting optimization problem is inherently a gradient-descent method and is sensitive to initialization. The resulting solution is a local optimum in the neighborhood of the initial guess. This sensitivity to initialization presents a significant challenge in clustering large data sets into many clusters. In this paper, we present a different approach to approximate mixture fitting for clustering. We introduce an exemplar-based likelihood function that approximates the exact likelihood. This formulation leads to a convex minimization problem and an efficient algorithm with guaranteed convergence to the globally optimal solution. The resulting clustering can be thought of as a probabilistic mapping of the data points to the set of exemplars that minimizes the average distance and the information-theoretic cost of mapping. We present experimental results illustrating the performance of our algorithm and its comparison with the conventional approach to mixture model clustering.
We sought to identify the functional correlates of reduced magnetic resonance imaging (MRI) volumes of the superior temporal gyrus (STG) and the fusiform gyrus (FG) in patients with chronic schizophrenia. MRI volumes, positive/negative symptoms, and neuropsychological tests of facial memory and executive functioning were examined within the same subjects. The results indicated two distinct, dissociable brain structure-function relationships: (1) reduced left STG volume-positive symptoms-executive deficits; (2) reduced left FG-negative symptoms-facial memory deficits. STG and FG volume reductions may each make distinct contributions to symptoms and cognitive deficits of schizophrenia.
Malformations of the cerebral cortex are recognized as a common cause of developmental delay, neurological deficits, mental retardation and epilepsy. Currently, the diagnosis of cerebral cortical malformations is based on a subjective interpretation of neuroimaging characteristics of the cerebral gray matter and underlying white matter. There is no automated system for aiding the observer in making the diagnosis of a cortical malformation. In this paper a fuzzy rule-based system is proposed as a solution for this problem. The system collects the available expert knowledge about cortical malformations and assists the medical observer in arriving at a correct diagnosis. Moreover, the system allows the study of the influence of the various factors that take part in the decision. The evaluation of the system has been carried out by comparing the automated diagnostic algorithm with known case examples of various malformations due to abnormal cortical organization. An exhaustive evaluation of the system by comparison with published cases and a ROC analysis is presented in the paper.
In this paper, we describe a method for segmenting fiber bundles from diffusion-weighted magnetic resonance images using a locally-constrained region based approach. From a pre-computed optimal path, the algorithm propagates outward capturing only those voxels which are locally connected to the fiber bundle. Rather than attempting to find large numbers of open curves or single fibers, which individually have questionable meaning, this method segments the full fiber bundle region. The strengths of this approach include its ease-of-use, computational speed, and applicability to a wide range of fiber bundles. In this work, we show results for segmenting the cingulum bundle. Finally, we explain how this approach and extensions thereto overcome a major problem that typical region-based flows experience when attempting to segment neural fiber bundles.
OBJECTIVE: The aim of this study was to evaluate the longitudinal reproducibility of cartilage volume and surface area measurements in moderate osteoarthritis (OA) of the knee.
MATERIALS AND METHODS: We analysed 5 MRI (GE 1.5T, sagittal 3D SPGR) data sets of patients with osteoarthritis (OA) of the knee (Kellgren Lawrence grade I-II). Two scans were performed: one baseline scan and one follow-up scan 3 months later (96 +/- 10 days). For segmentation, 3D Slicer 2.5 software was used. Two segmentations were performed by two readers independently who were blinded to the scan dates. Tibial and femoral cartilage volume and surface were determined. Longitudinal and cross-sectional precision errors were calculated using the standard deviation (SD) and coefficient of variation (CV%=100x[SD/mean]) from the repeated measurements in each patient. The in vivo reproducibility was then calculated as the root mean square of these individual reproducibility errors.
RESULTS: The cross-sectional root mean squared coefficient of variation (RMSE-CV) was 1.2, 2.2 and 2.4% for surface area measurements (femur, medial and lateral tibia respectively) and 1.4, 1.8 and 1.3% for the corresponding cartilage volumes. Longitudinal RMSE-CV was 3.3, 3.1 and 3.7% for the surface area measurements (femur, medial and lateral tibia respectively) and 2.3, 3.3 and 2.4% for femur, medial and lateral tibia cartilage volumes.
CONCLUSION: The longitudinal in vivo reproducibility of cartilage surface and volume measurements in the knee using this segmentation method is excellent. To the best of our knowledge we measured, for the first time, the longitudinal reproducibility of cartilage volume and surface area in participants with mild to moderate OA.
OBJECTIVE: The usefulness of neurosurgical navigation with current visualizations is seriously compromised by brain shift, which inevitably occurs during the course of the operation, significantly degrading the precise alignment between the pre-operative MR data and the intra-operative shape of the brain. Our objectives were (i) to evaluate the feasibility of non-rigid registration that compensates for the brain deformations within the time constraints imposed by neurosurgery, and (ii) to create augmented reality visualizations of critical structural and functional brain regions during neurosurgery using pre-operatively acquired fMRI and DT-MRI. MATERIALS AND METHODS: Eleven consecutive patients with supratentorial gliomas were included in our study. All underwent surgery at our intra-operative MR imaging-guided therapy facility and have tumors in eloquent brain areas (e.g. precentral gyrus and cortico-spinal tract). Functional MRI and DT-MRI, together with MPRAGE and T2w structural MRI were acquired at 3 T prior to surgery. SPGR and T2w images were acquired with a 0.5 T magnet during each procedure. Quantitative assessment of the alignment accuracy was carried out and compared with current state-of-the-art systems based only on rigid registration. RESULTS: Alignment between pre-operative and intra-operative datasets was successfully carried out during surgery for all patients. Overall, the mean residual displacement remaining after non-rigid registration was 1.82 mm. There is a statistically significant improvement in alignment accuracy utilizing our non-rigid registration in comparison to the currently used technology (p<0.001). CONCLUSIONS: We were able to achieve intra-operative rigid and non-rigid registration of (1) pre-operative structural MRI with intra-operative T1w MRI; (2) pre-operative fMRI with intra-operative T1w MRI, and (3) pre-operative DT-MRI with intra-operative T1w MRI. The registration algorithms as implemented were sufficiently robust and rapid to meet the hard real-time constraints of intra-operative surgical decision making. The validation experiments demonstrate that we can accurately compensate for the deformation of the brain and thus can construct an augmented reality visualization to aid the surgeon.
Schizophrenia is characterized by deficits in cognition as well as visual perception. There have, however, been few magnetic resonance imaging (MRI) studies of the occipital lobe as an anatomically defined region of interest in schizophrenia. To examine whether or not patients with chronic schizophrenia show occipital lobe volume abnormalities, we measured gray matter volumes for both the primary visual area (PVA) and the visual association areas (VAA) using MRI based neuroanatomical landmarks and three-dimensional information. PVA and VAA gray matter volumes were measured using high-spatial resolution MRI in 25 male patients diagnosed with chronic schizophrenia and in 28 male normal controls. Chronic schizophrenia patients showed reduced bilateral VAA gray matter volume (11%), compared with normal controls, whereas patients showed no group difference in PVA gray matter volume. These results suggest that reduced bilateral VAA may be a neurobiological substrate of some of the deficits observed in early visual processing in schizophrenia.
We present three different sequential Wiener filters, namely, isotropic, orientation and anisotropic. The first one is similar to the classical Wiener filter in the sense that it uses an isotropic neighborhood to estimate its parameters. Here we present a sequential version of it. The orientation Wiener filter uses oriented neighborhoods to estimate the structure orientation present at each voxel, giving rise to a modified estimator of the parameters. Finally, the anisotropic Wiener filter combines both approaches adaptively so that the appropriate approach is locally selected. Several synthetic experiments are presented showing the performance of the filters with respect to their parameters. A mean square error analysis is performed using a publicly available magnetic resonance imaging (MRI) brain phantom and a comparison with other filtering approaches is carried out. In addition, results from filtering real MRI data are presented.
Bayesian classification methods have been extensively used in a variety of image processing applications, including medical image analysis. The basic procedure is to combine data-driven knowledge in the likelihood terms with clinical knowledge in the prior terms to classify an image into a pre-determined number of classes. In many applications, it is difficult to construct meaningful priors and, hence, homogeneous priors are assumed. In this paper, we show how expectation-maximization weights and neighboring posterior probabilities may be combined to make intuitive use of the Bayesian priors. Drawing upon insights from computer vision tracking algorithms, we cast the problem in a tissue tracking framework. We show results of our algorithm on the classification of gray and white matter along with surrounding cerebral spinal fluid in brain MRI scans. We show results of our algorithm on 20 brain MRI datasets along with validation against expert manual segmentations.
In vivo quantification of neuroanatomical shape variations is possible due to recent advances in medical imaging and has proven useful in the study of neuropathology and neurodevelopment. In this paper, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRIs) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, which allows us to characterize the order of development of large-scale and finer folding patterns independently. Given a limited amount of training data, we use a regularization framework to estimate the parameters of the Gompertz model to improve the prediction performance on new data. We develop an efficient method to estimate this regularized Gompertz model based on the Broyden-Fletcher-Goldfarb-Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomic information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurologic deficits in newborns.
The integration of medical devices with software applications is crucial for image-guided medical applications. This work describes a general device interface that has been designed for high-frequency streaming of multi-modal events, thus providing maximum performance and flexibility for such applications. Several sample applications and performance tests are provided to demonstrate the usability of the concept.
This paper presents a novel multiscale shape representation and segmentation algorithm based on the spherical wavelet transform. This work is motivated by the need to compactly and accurately encode variations at multiple scales in the shape representation in order to drive the segmentation and shape analysis of deep brain structures, such as the caudate nucleus or the hippocampus. Our proposed shape representation can be optimized to compactly encode shape variations in a population at the needed scale and spatial locations, enabling the construction of more descriptive, nonglobal, nonuniform shape probability priors to be included in the segmentation and shape analysis framework. In particular, this representation addresses the shortcomings of techniques that learn a global shape prior at a single scale of analysis and cannot represent fine, local variations in a population of shapes in the presence of a limited dataset. Specifically, our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population. We further refine the shape representation by separating into groups wavelet coefficients that describe independent global and/or local biological variations in the population, using spectral graph partitioning. We then learn a prior probability distribution induced over each group to explicitly encode these variations at different scales and spatial locations. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to two different brain structures, the caudate nucleus and the hippocampus, of interest in the study of schizophrenia. We show: 1) a reconstruction task of a test set to validate the expressiveness of our multiscale prior and 2) a segmentation task. In the reconstruction task, our results show that for a given training set size, our algorithm significantly improves the approximation of shapes in a testing set over the Point Distribution Model, which tends to oversmooth data. In the segmentation task, our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm, by capturing finer shape details.
In this paper a comprehensive framework for pre-operative planning, procedural skill training, and intraoperative navigation is presented. The goal of this system is to integrate surgical simulation with surgical planning in order to improve the individual treatment of patients. Various surgical approaches and new, more complex procedures can be assessed using a safe and objective platform that will allow the physicians to explore and discuss possible risks and benefits prior to the intervention. A simulation environment extends the pre-operative planning in a natural way, as it allows for direct evaluation of the surgical approach envisioned for each case. In addition, by providing intraoperative navigation based on this simulation, surgeons can carry out the previously optimized plan with higher precision and greater confidence.