28 research outputs found

    REGISTRATION AND SEGMENTATION OF BRAIN MR IMAGES FROM ELDERLY INDIVIDUALS

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    Quantitative analysis of the MRI structural and functional images is a fundamental component in the assessment of brain anatomical abnormalities, in mapping functional activation onto human anatomy, in longitudinal evaluation of disease progression, and in computer-assisted neurosurgery or surgical planning. Image registration and segmentation is central in analyzing structural and functional MR brain images. However, due to increased variability in brain morphology and age-related atrophy, traditional methods for image registration and segmentation are not suitable for analyzing MR brain images from elderly individuals. The overall goal of this dissertation is to develop algorithms to improve the registration and segmentation accuracy in the geriatric population. The specific aims of this work includes 1) to implement a fully deformable registration pipeline to allow a higher degree of spatial deformation and produce more accurate deformation field, 2) to propose and validate an optimum template selection method for atlas-based segmentation, 3) to propose and validate a multi-template strategy for image normalization, which characterizes brain structural variations in the elderly, 4) to develop an automated segmentation and localization method to access white matter integrity (WMH) in the elderly population, and finally 5) to study the default-mode network (DMN) connectivity and white matter hyperintensity in late-life depression (LLD) with the developed registration and segmentation methods. Through a series of experiments, we have shown that the deformable registration pipeline and the template selection strategies lead to improved accuracy in the brain MR image registration and segmentation, and the automated WMH segmentation and localization method provides more specific and more accurate information about volume and spatial distribution of WMH than traditional visual grading methods. Using the developed methods, our clinical study provides evidence for altered DMN connectivity in LLD. The correlation between WMH volume and DMN connectivity emphasizes the role of vascular changes in LLD's etiopathogenesis

    PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation

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    With the advent of convolutional neural networks~(CNN), supervised learning methods are increasingly being used for whole brain segmentation. However, a large, manually annotated training dataset of labeled brain images required to train such supervised methods is frequently difficult to obtain or create. In addition, existing training datasets are generally acquired with a homogeneous magnetic resonance imaging~(MRI) acquisition protocol. CNNs trained on such datasets are unable to generalize on test data with different acquisition protocols. Modern neuroimaging studies and clinical trials are necessarily multi-center initiatives with a wide variety of acquisition protocols. Despite stringent protocol harmonization practices, it is very difficult to standardize the gamut of MRI imaging parameters across scanners, field strengths, receive coils etc., that affect image contrast. In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input acquisition. Our approach relies on building approximate forward models of pulse sequences that produce a typical test image. For a given pulse sequence, we use its forward model to generate plausible, synthetic training examples that appear as if they were acquired in a scanner with that pulse sequence. Sampling over a wide variety of pulse sequences results in a wide variety of augmented training examples that help build an image contrast invariant model. Our method trains a single CNN that can segment input MRI images with acquisition parameters as disparate as T1T_1-weighted and T2T_2-weighted contrasts with only T1T_1-weighted training data. The segmentations generated are highly accurate with state-of-the-art results~(overall Dice overlap=0.94=0.94), with a fast run time~(\approx 45 seconds), and consistent across a wide range of acquisition protocols.Comment: Typo in author name corrected. Greves -> Grev

    Effect of Divalproex on Brain Morphometry, Chemistry, and Function in Youth at High-Risk for Bipolar Disorder: A Pilot Study

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    Abstract Objective: Divalproex has been found efficacious in treating adolescents with and at high risk for bipolar disorder (BD), but little is known about the effects of mood stabilizers on the brain itself. We sought to examine the effects of divalproex on the structure, chemistry, and function of specific brain regions in children at high-risk for BD. Methods: A total of 24 children with mood dysregulation but not full BD, all offspring of a parent with BD, were treated with divalproex monotherapy for 12 weeks. A subset of 11 subjects and 6 healthy controls were scanned with magnetic resonance imaging (MRI, magnetic resonance spectroscopy [MRS], and functional MRI [fMRI]) at baseline and after 12 weeks. Results: There were no significant changes in amygdalar or cortical volume found over 12 weeks. Furthermore, no changes in neurometabolite ratios were found. However, we found the degree of decrease in prefrontal brain activation to correlate with degree of decrease in depressive symptom severity. Conclusions: Bipolar offspring at high risk for BD did not show gross morphometric, neurometabolite, or functional changes after 12 weeks of treatment with divalproex. Potential reasons include small sample size, short exposure to medications, or lack of significant neurobiological impact of divalproex in this particular population

    Development of Korean Standard Brain Templates

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    We developed age, gender and ethnic specific brain templates based on MR and Positron-Emission Tomography (PET) images of Korean normal volunteers. Seventy-eight normal right-handed volunteers (M/F=49/29) underwent 3D T1-weighted SPGR MR and F-18-FDG PET scans. For the generation of standard templates, an optimal target brain that has the average global hemispheric shape was selected for each gender. MR images were then spatially normalized by linear transformation to the target brains, and normalization parameters were reapplied to PET images. Subjects were subdivided into 2 groups for each gender: the young/midlife (<55 yr) and the elderly groups. Young and elderly MRI/PET templates were composed by averaging the spatially normalized images. Korean templates showed different shapes and sizes (mean length, width, and height of the brains were 16.5, 14.3 and 12.1 cm for man, and 15.6, 13.5 and 11.4 cm for woman) from the template based on Caucasian (18.3, 14.2, and 13.3 cm). MRI and PET templates developed in this study will provide the framework for more accurate stereotactic standardization and anatomical localization.ope

    Assessment and optimisation of MRI measures of atrophy as potential markers of disease progression in multiple sclerosis

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    There is a need for sensitive measures of disease progression in multiple sclerosis (MS) to monitor treatment effects and understand disease evolution. MRI measures of brain atrophy have been proposed for this purpose. This thesis investigates a number of measurement techniques to assess their relative ability to monitor disease progression in clinically isolated syndromes (CIS) and early relapsing remitting MS (RRMS). Presented, is work demonstrating that measurement techniques and MR acquisitions can be optimised to give small but significant improvements in measurement sensitivity and precision, which provided greater statistical power. Direct comparison of numerous techniques demonstrated significant differences between them. Atrophy measurements from SIENA and the BBSI (registration-based techniques) were significantly more precise than segmentation and subtraction of brain volumes, although larger percentage losses were observed in grey matter fraction. Ventricular enlargement (VE) gave similar statistical power and these techniques were robust and reliable; scan-rescan measurement error was <0.01% of brain volume for BBSI and SIENA and <0.04ml for VE. Annual atrophy rates (using SIENA) were -0.78% in RRMS and -0.52% in CIS patients who progressed to MS, which were significantly greater than the rate observed in controls (-0.07%). Sample size calculations for future trials of disease-modifying treatments in RRMS, using brain atrophy as an outcome measure, are described. For SIENA, the BBSI and VE respectively, an estimated 123, 157 and 140 patients per treatment arm respectively would be required to show a 30% slowing of atrophy rate over two years. In CIS subjects brain atrophy rate was a significant prognostic factor, independent of T2 MRI lesions at baseline, for development of MS by five year follow-up. It was also the most significant MR predictor of disability in RRMS subjects. Cognitive assessment of RRMS patients at five year follow-up is described, and brain atrophy rate was a significant predictor of overall cognitive performance, and more specifically, of performance in tests of memory. The work in this thesis has identified methods for sensitively measuring progressive brain atrophy in MS. It has shown that brain atrophy changes in early MS are related to early clinical evolution, providing complementary information to clinical assessment that could be utilised to monitor disease progression

    Automated brain segmentation methods for clinical quality MRI and CT images

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    Alzheimer’s disease (AD) is a progressive neurodegenerative disorder associated with brain tissue loss. Accurate estimation of this loss is critical for the diagnosis, prognosis, and tracking the progression of AD. Structural magnetic resonance imaging (sMRI) and X-ray computed tomography (CT) are widely used imaging modalities that help to in vivo map brain tissue distributions. As manual image segmentations are tedious and time-consuming, automated segmentation methods are increasingly applied to head MRI and head CT images to estimate brain tissue volumes. However, existing automated methods can be applied only to images that have high spatial resolution and their accuracy on heterogeneous low-quality clinical images has not been tested. Further, automated brain tissue segmentation methods for CT are not available, although CT is more widely acquired than MRI in the clinical setting. For these reasons, large clinical imaging archives are unusable for research studies. In this work, we identify and develop automated tissue segmentation and brain volumetry methods that can be applied to clinical quality MRI and CT images. In the first project, we surveyed the current MRI methods and validated the accuracy of these methods when applied to clinical quality images. We then developed CTSeg, a tissue segmentation method for CT images, by adopting the MRI technique that exhibited the highest reliability. CTSeg is an atlas-based statistical modeling method that relies on hand-curated features and cannot be applied to images of subjects with different diseases and age groups. Advanced deep learning-based segmentation methods use hierarchical representations and learn complex features in a data-driven manner. In our final project, we develop a fully automated deep learning segmentation method that uses contextual information to segment clinical quality head CT images. The application of this method on an AD dataset revealed larger differences between brain volumes of AD and control subjects. This dissertation demonstrates the potential of applying automated methods to large clinical imaging archives to answer research questions in a variety of studies

    Multimodal image analysis of the human brain

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    Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

    Segmentation of neuroanatomy in magnetic resonance images

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    Segmentation in neurological Magnetic Resonance Imaging (MRI) is necessary for volume measurement, feature extraction and for the three-dimensional display of neuroanatomy. This thesis proposes several automated and semi-automated methods which offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. Work has concentrated on the use of dual echo multi-slice spin-echo data sets in order to take advantage of the intrinsically multi-parametric nature of MRI. Such data is widely acquired clinically and segmentation therefore does not require additional scans. The literature has been reviewed. Factors affecting image non-uniformity for a modem 1.5 Tesla imager have been investigated. These investigations demonstrate that a robust, fast, automatic three-dimensional non-uniformity correction may be applied to data as a pre-processing step. The merit of using an anisotropic smoothing method for noisy data has been demonstrated. Several approaches to neurological MRI segmentation have been developed. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing, two threshold based techniques and a fast radial CSF identification approach are proposed to identify the intracranial region contour in each slice of the data set. Once isolated, the intracranial region is further processed to identify CSF, and, depending upon the MRI pulse sequence used, the brain itself may be sub-divided into grey matter and white matter using semiautomatic contrast enhancement and clustering methods. The segmentation of Multiple Sclerosis (MS) plaques has also been considered. The utility of the stack, a data driven multi-resolution approach to segmentation, has been investigated, and several improvements to the method suggested. The factors affecting the intrinsic accuracy of neurological volume measurement in MRI have been studied and their magnitudes determined for spin-echo imaging. Geometric distortion - both object dependent and object independent - has been considered, as well as slice warp, slice profile, slice position and the partial volume effect. Finally, the accuracy of the approaches to segmentation developed in this thesis have been evaluated. Intracranial volume measurements are within 5% of expert observers' measurements, white matter volumes within 10%, and CSF volumes consistently lower than the expert observers' measurements due to the observers' inability to take the partial volume effect into account

    MRI measures of brain integrity and their relation to processing speed in the elderly

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    A significant percentage of the elderly population experiences at least one geriatric disability. Previous studies have shown that geriatric disabilities are preceded by sub-clinical cognitive changes of aging and brain changes seen on magnetic resonance imaging (MRI). Decreased information processing speed has been identified as a common factor associated with age-related disabilities in gait, cognition, and mood. However, the current neurocognitive model of aging is incomplete; there remains uncertainty about the relationships between the different components of brain integrity and cognitive function. The goals of this dissertation are to characterize the relationships between different functional and structural MRI markers (for example: macro-structural, micro-structural, physiologic) with respect to cognitive aging and to improve the neuroimaging toolset for oldest old.We studied the relationship between functional MRI markers, structural MRI markers, and information processing speed in a sample of twenty-five healthy elderly subjects. We found that recruitment of fronto-parietal brain areas was associated with higher performance. Also, greater structural damage (white matter integrity) was associated with lower activation in prefrontal and anterior cingulate regions. In the presence of underlying brain connectivity structural abnormalities, additional posterior parietal activation was found to be important for maintaining higher task performance.MRI MEASURES OF BRAIN INTEGRITY AND THEIR RELATION TO PROCESSING SPEED IN THE ELDERLYVijay Krishna Venkatraman, Ph.D.University of Pittsburgh, 2010vWe also studied MRI measures of brain structure in a sample of 277 community-dwelling older adults free from neurological diseases. This study used a set of neuroimage analysis pathways optimized for the MRI images and examined the macro- and micro-structural indices. The results indicate that both the macro- and micro-structural MRI indices may provide complementary information on neuroanatomical correlates of information processing speed. The micro-structural MRI indices of white matter integrity were found to be the strongest correlate of information processing speed in this sample.While developing the image analysis pipelines for this dataset, we noticed that the diffusion tensor-imaging pathway was particularly sensitive to the approach of localizing the white matter tracts. We used both empirical and simulated datasets to confirm our hypothesis that the mean fractional anisotropy of the white matter tract is more sensitive to individual differences in the elderly when compared to a skeleton based approach

    Improving nonlinear search with Self-Organizing Maps - Application to Magnetic Resonance Relaxometry

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    Quantification of myelin in vivo is crucial for the understanding of neurological diseases, like multiple sclerosis (MS). Multi-Component Driven Equilibrium Single Pulse Observation T1 and T2 (mcDESPOT) is a rapid and precise method for determination of the longitudinal and transverse relaxation times in a voxel wise fashion. Briefly, mcDESPOT couples sets of SPGR (spoiled gradient-recalled echo) and bSSFP (fully balance steady-state free precession) data acquired over a range of flip angles (α) with constant interpulse spacing (TR) to derive 6 parameters (free-water T1 and T2, myelin-associated water T1 and T2, relative myelin-associated water volume fraction, and the myelin-associated water proton residence time) based on water exchange models. However, this procedure is computationally expensive and extremely difficult due to the need to find the best fit to the 24 MRI signals volumes in a search of nonlinear 6 dimensional space of model parameters. In this context, the aim of this work is to improve mcDESPOT efficiency and accuracy using tissue information contained in the sets of signals (SPGR and bSSFP) acquired. The basic hypothesis is that similar acquired signals are referred to tissue portions with close features, which translate in similar parameters. This similarity could be used to drive the nonlinear mcDESPOT fitting, leading the optimization algorithm (that is based on a stochastic region contraction approach) to look for a solution (i.e. the 6 parameters vector) also in regions defined by previously computed solutions of others voxels with similar signals. For this reason, we clustered the sets of SPGR and bSSFP using the neural network called Self Organizing Map (SOM), which uses a competitive learning technique to train itself in an unsupervised manner. The similarity information obtained from the SOM was then used to accordingly suggest solutions to the optimization algorithm. A first validation phase with in silico data was performed to evaluate the performances of the SOM and of the modified method, SOM+mcDESPOT. The latter was further validated using real magnetic resonance images. The last step consisted of applying the SOM+mcDESPOT to a group of healthy subjects ( ) and a group of MS patients ( ) to look for differences in myelin-associated water fractions values between the two groups. The validation phases with in silico data verified the initial hypothesis: in more the 74% of the times, the correct solution of a certain voxel is in the space dictated by the cluster which that voxel is mapped to. Adding the information of similar solutions extracted from that cluster helps to improve the signals fitting and the accuracy in the determination of the 7 parameters. This result is still present even if the data are corrupted by a high level of noise (SNR=50). Using real images allowed to confirm the power of SOM+mcDESPOT underlined through the in silico data. The application of SOM+mcDESPOT to the controls and to the MS patients allowed firstly obtaining more feasible results than the traditional mcDESPOT. Moreover, a statistically significant difference of the myelin-associated water fraction values in the normal appearing white matter was found between the two groups: the MS patients, in fact, show lower fraction values compared to the normal subjects, indicating an abnormal presence of myelin in the normal appearing white matter of MS patients. In conclusion, we proposed the novel method SOM+mcDESPOT that is able to extract and exploit the information contained in the MRI signals to drive appropriately the optimization algorithm implemented in mcDESPOT. In so doing, the overall accuracy of the method in both the signals fitting and in the determination of the 7 parameters improves. Thus, the outstanding potentiality of SOM+mcDESPOT could assume a crucial role in improving the indirect quantification of myelin in both healthy subjects and patient
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