Multi-Scale Modeling for Image Analysis of Brain Tumor Studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

A survey of MRI-based medical image analysis for brain tumor studies

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

FUNCTIONAL PRINCIPAL COMPONENTS MODEL FOR HIGH-DIMENSIONAL BRAIN IMAGING

We establish a fundamental equivalence between singular value decomposition (SVD) and functional principal components analysis (FPCA) models. The constructive relationship allows to deploy the numerical efficiency of SVD to fully estimate the components of FPCA, even for extremely high-dimensional functional objects, such as brain images. As an example, a functional mixed effect model is fitted to high-resolution morphometric (RAVENS) images. The main directions of morphometric variation in brain volumes are identified and discussed.

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.

Statistically assisted fluid registration algorithm - SAFIRA

In this paper, we develop and validate a new Statistically Assisted Fluid Registration Algorithm (SAFIRA) for brain images. A non-statistical version of this algorithm was first implemented in [2] and re-formulated using Lagrangian mechanics in [3]. Here we extend this algorithm to 3D: given 3D brain images from a population, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the non-statistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the regularizing (i.e., the non-conservative Lagrangian) terms, creating four versions of the algorithm. We evaluated the accuracy of each algorithm variant using the manually labeled LPBA40 dataset, which provides us with ground truth anatomical segmentations. We also compared the power of the different algorithms using tensor-based morphometry -a technique to analyze local volumetric differences in brain structure- applied to 46 3D brain scans from healthy monozygotic twins.

A nonconservative Lagrangian framework for statistical fluid registration - SAFIRA

In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented, where the deformation was regularized by penalizing deviations from a zero rate of strain. In, the terms regularizing the deformation included the covariance of the deformation matrices Σ and the vector fields (q). Here, we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3-D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the nonconservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms' performance on 92 3-D brain scans from healthy monozygotic and dizygotic twins; 2-D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for large-scale neuroimaging studies.

Genetics of anisotropy asymmetry: Registration and sample size effects

Brain asymmetry has been a topic of interest for neuroscientists for many years. The advent of diffusion tensor imaging (DTI) allows researchers to extend the study of asymmetry to a microscopic scale by examining fiber integrity differences across hemispheres rather than the macroscopic differences in shape or structure volumes. Even so, the power to detect these microarchitectural differences depends on the sample size and how the brain images are registered and how many subjects are studied. We fluidly registered 4 Tesla DTI scans from 180 healthy adult twins (45 identical and fraternal pairs) to a geometrically-centered population mean template. We computed voxelwise maps of significant asymmetries (left/right hemisphere differences) for common fiber anisotropy indices (FA, GA). Quantitative genetic models revealed that 47-62% of the variance in asymmetry was due to genetic differences in the population. We studied how these heritability estimates varied with the type of registration target (T1- or T2-weighted) and with sample size. All methods consistently found that genetic factors strongly determined the lateralization of fiber anisotropy, facilitating the quest for specific genes that might influence brain asymmetry and fiber integrity.

How does angular resolution affect diffusion imaging measures?

A key question in diffusion imaging is how many diffusion-weighted images suffice to provide adequate signal-to-noise ratio (SNR) for studies of fiber integrity. Motion, physiological effects, and scan duration all affect the achievable SNR in real brain images, making theoretical studies and simulations only partially useful. We therefore scanned 50 healthy adults with 105-gradient high-angular resolution diffusion imaging (HARDI) at 4T. From gradient image subsets of varying size (6 ≤ N ≤ 94) that optimized a spherical angular distribution energy, we created SNR plots (versus gradient numbers) for seven common diffusion anisotropy indices: fractional and relative anisotropy (FA, RA), mean diffusivity (MD), volume ratio (VR), geodesic anisotropy (GA), its hyperbolic tangent (tGA), and generalized fractional anisotropy (GFA). SNR, defined in a region of interest in the corpus callosum, was near-maximal with 58, 66, and 62 gradients for MD, FA, and RA, respectively, and with about 55 gradients for GA and tGA. For VR and GFA, SNR increased rapidly with more gradients. SNR was optimized when the ratio of diffusion-sensitized to non-sensitized images was 9.13 for GA and tGA, 10.57 for FA, 9.17 for RA, and 26 for MD and VR. In orientation density functions modeling the HARDI signal as a continuous mixture of tensors, the diffusion profile reconstruction accuracy rose rapidly with additional gradients. These plots may help in making trade-off decisions when designing diffusion imaging protocols.

Serial Changes in Markers Measuring Coagulation, Fibrinolysis, and Vasoactivity in Patients with Ischemic Stroke

Ischemic stroke (IS) is a heterogeneous disease in which outcome is influenced by many factors. The hemostatic system is activated in association with cerebral ischemia, and thus, markers measuring coagulation, fibrinolysis, and vasoactivity could be useful tools in clinical practice. We investigated whether repeated measurements of these markers reveal patterns that might help in evaluating IS patients, including the early diagnosis of stroke subtypes, in estimating prognosis and risk of recurrence, and in selecting a treatment for secondary prevention of stroke. Vasoconstrictor peptide endothelin-1 (ET-1), homocysteine (Hcy), indicators of thrombin formation and activation (prothrombin fragment 1+2/F1+2, thrombin-antithrombin complex/TAT), indicators of plasmin formation and fibrinolysis (tissue plasminogen activator/t-PA, plasminogen activator inhibitor-1/PAI-1, and D-dimer), and natural anticoagulants (antithrombin/AT, protein C/PC, and protein S/PS) were measured in 102 consecutive mild to moderate IS patients on four occasions: on admission and at 1 week, 1 month, and 3 months after stroke, and once in controls. All patients underwent neurological examination and blood sampling in the same session. Furthermore, 42 IS patients with heterozygous factor V Leiden mutation (FVLm) were selected from 740 IS patients without an obvious etiology, and evaluated in detail for specific clinical, laboratory, and radiological features. Measurements of ET-1 and Hcy levels did not disclose information that could aid in the diagnostic evaluation of IS patients. F1+2 level at 3 months after IS had a positive correlation with recurrence of thromboembolic events, and thus, may be used as a predictive marker of subsequent cerebral events. The D-dimer and AT levels on admission and 1 week after IS were strongly associated with stroke severity, outcome, and disability. The specific analysis of IS patients with FVLm more often revealed a positive family history of thrombosis, a higher prevalence of peripheral vascular disease, and multiple infarctions in brain images, most of which were `silent infarcts´. Results of this study support the view that IS patients with sustained activation of both the fibrinolytic and the coagulation systems and increased thrombin generation may have an unfavorable prognosis. The level of activation may reflect the ongoing thrombotic process and the extent of thrombosis. Changes in these markers could be useful in predicting prognosis of IS patients. A clear need exists for a randomized prospective study to determine whether a subgroup of IS patients with markers indicating activation of fibrinolytic and coagulation systems might benefit from more aggressive secondary prevention of IS.

Peritoneal dialysis and neurological outcome in infants and small children

Although improved outcomes for children on peritoneal dialysis (PD) have been seen in recent years, the youngest patients continue to demonstrate inferior growth, more frequent infections, more neurological sequelae, and higher mortality compared to older children. Also, maintain-ing normal intravascular volume status, especially in anuric patients, has proven difficult. This study was designed to treat and monitor these youngest PD patients, which are relatively many due to the high prevalence of congenital nephrotic syndrome of the Finnish type (CNF, NPHS1) in Finland, with a strict protocol, to evaluate the results and to improve metabolic balance, growth, and development. A retrospective analysis of 23 children under two years of age at onset of PD, treated between 1995 and 2000, was performed to obtain a control population for our prospective PD study. Respectively, 21 patients less than two years of age at the beginning of PD were enrolled in prospective studies between 2001 and 2005. Medication for uremia and nutrition were care-fully adjusted during PD. Laboratory parameters and intravascular volume status were regu-larly analyzed. Growth was analyzed and compared with midparental height. In a prospective neurological study, the risk factors for development and the neurological development was determined. Brain images were surveyed. Hearing was tested. In a retrospective neurological study, the data of six NPHS1 patients with a congruent neurological syndrome was analyzed. All these patients had a serious dyskinetic cerebral palsy-like syndrome with muscular dysto-nia and athetosis (MDA). They also had a hearing defect. Metabolic control was mainly good in both PD patient groups. Hospitalization time shortened clearly. The peritonitis rate diminished. Hypertension was a common problem. Left ventricular hypertrophy decreased during the prospective study period. None of the patients in either PD group had pulmonary edema or dialysis-related seizures. Growth was good and catch-up growth was documented in most patients in both patient groups during PD. Mortality was low (5% in prospective and 9% in retrospective PD patients). In the prospective PD patient group 11 patients (52%) had some risk factor for their neuro-development originating from the predialysis period. The neurological problems, detected be-fore PD, did not worsen during PD and none of the patients developed new neurological com-plications during PD. Brain infarcts were detected in four (19%) and other ischemic lesions in three patients (14%). At the end of this study, 29% of the prospectively followed patients had a major impairment of their neurodevelopment and 43% only minor impairment. In the NPHS1+MDA patients, no clear explanation for the neurological syndrome was found. The brain MRI showed increased signal intensity in the globus pallidus area. Kernic-terus was contemplated to be causative in the hypoproteinemic newborns but it could not be proven. Mortality was as high as 67%. Our results for young PD patients were promising. Metabolic control was acceptable and growth was good. However, the children were significantly smaller when compared to their midparental height. Although many patients were found to have neurological impairment at the end of our follow-up period, PD was a safe treatment whereby the neurodevelopment did not worsen during PD.

左中部梭状回枕颞沟内皮质对汉字加工的必要性、选择性和作用性质

A number of functional neuroimaging studies with skilled readers consistently showed activation to visual words in the left mid-fusiform cortex in occipitotemporal sulcus (LMFC-OTS). Neuropsychological studies also showed that lesions at left ventral occipitotemporal areas result in impairment in visual word processing. Based on these empirical observations and some theoretical speculations, a few researchers postulated that the LMFC-OTS is responsible for instant parallel and holistic extraction of the abstract representation of letter strings, and labeled this piece of cortex as “visual word form area” (VWFA). Nonetheless, functional neuroimaging studies alone is basically a correlative rather than causal approach, and lesions in the previous studies were typically not constrained within LMFC-OTS but also involving other brain regions beyond this area. Given these limitations, it remains unanswered for three fundamental questions: is LMFC-OTS necessary for visual word processing? is this functionally selective for visual word processing while unnecessary for processing of non-visual word stimuli? what are its function properties in visual word processing? This thesis aimed to address these questions through a series of neuropsychological, anatomical and functional MRI experiments in four patients with different degrees of impairments in the left fusiform gyrus. Necessity: Detailed analysis of anatomical brain images revealed that the four patients had differential foci of brain infarction. Specifically, the LMFC-OTS was damaged in one patient, while it remained intact in the other three. Neuropsychological experiments showed that the patient with lesions in the LMFC-OTS had severe impairments in reading aloud and recognizing Chinese characters, i.e., pure alexia. The patient with intact LMFC-OTS but information from the left visual field (LVF) was blocked due to lesions in the splenium of corpus callosum, showed impairment in Chinese characters recognition when the stimuli were presented in the LVF but not in the RVF, i.e. left hemialexia. In contrast, the other two patients with intact LMFC-OTS had normal function in processing Chinese characters. The fMRI experiments demonstrated that there was no significant activation to Chinese characters in the LMFC-OTS of the pure alexic patient and of the patient with left hemialexia when the stimuli were presented in the LVF. On the other hand, this patient, when Chinese characters were presented in right visual field, and the other two with intact LMFC-OTS had activation in the LMFC-OTS. These results together point to the necessity of the LMFC-OTS for Chinese character processing. Selectivity: We tested selectivity of the LMFC-OTS for visual word processing through systematically examining the patients’ ability for processing visual vs. auditory words, and word vs. non-word visual stimuli, such as faces, objects and colors. Results showed that the pure alexic patients could normally process auditory words (expression, understanding and repetition of orally presented words) and non-word visual stimuli (faces, objects, colors and numbers). Although the patient showed some impairments in naming faces, objects and colors, his performance scores were only slightly lower or not significantly different relative to those of the patients with intact LMFC-OTS. These data provide compelling evidence that the LMFC-OTS is not requisite for processing non-visual word stimuli, thus has selectivity for visual word processing. Functional properties: With tasks involving multiple levels and aspects of word processing, including Chinese character reading, phonological judgment, semantic judgment, identity judgment of abstract visual word representation, lexical decision, perceptual judgment of visual word appearance, and dictation, copying, voluntary writing, etc., we attempted to reveal the most critical dysfunction caused by damage in the LMFC-OTS, thus to clarify the most essential function of this region. Results showed that in addition to dysfunctions in Chinese character reading, phonological and semantic judgment, the patient with lesions at LMFC-OTS failed to judge correctly whether two characters (including compound and simple characters) with different surface features (e.g., different fonts, printed vs. handwritten vs. calligraphy styles, simplified characters vs. traditional characters, different orientations of strokes or whole characters) had the same abstract representation. The patient initially showed severe impairments in processing both simple characters and compound characters. He could only copy a compound character in a stroke-by-stroke manner, but not by character-by-character or even by radical-by-radical manners. During the recovery process, namely five months later, the patient could complete the abstract representation tasks of simple characters, but showed no improvement for compound characters. However, he then could copy compound characters in a radical-by-radical manner. Furthermore, it seems that the recovery of copying paralleled to that of judgment of abstract representation. These observations indicate that lesions of the LMFC-OTS in the pure alexic patients caused several damage in the ability of extracting the abstract representation from lower level units to higher level units, and the patient had especial difficulty to extract the abstract representation of whole character from its secondary units (e.g., radicals or single characters) and this ability was resistant to recover from impairment. Therefore, the LMFC-OTS appears to be responsible for the multilevel (particularly higher levels) abstract representations of visual word form. Successful extraction seems independent on access to phonological and semantic information, given the alexic patient showed severe impairments in reading aloud and semantic processing on simple characters while maintenance of intact judgment on their abstract representation. However, it is also possible that the interaction between the abstract representation and its related information e.g. phonological and semantic information was damaged as well in this patient. Taken together, we conclude that: 1) the LMFC-OTS is necessary for Chinese character processing, 2) it is selective for Chinese character processing, and 3) its critical function is to extract multiple levels of abstract representation of visual word and possibly to transmit it to phonological and semantic systems.

Neural correlates of task switching in paternal 15q11-q13 deletion Prader-Willi syndrome

We report a first study of brain activity linked to task switching in individuals with Prader-Willi syndrome (PWS) PWS individuals show a specific cognitive deficit in task switching which may be associated with the display of temper outbursts and repetitive questioning The performance of participants with PWS and typically developing controls was matched in a cued task switching procedure and brain activity was contrasted on switching and non switching blocks using SARI Individuals with PWS did not show the typical frontal-parietal pattern of neural activity associated with switching blocks, with significantly reduced activation in regions of the posterior parietal and ventromedial prefrontal cortices We suggest that this is linked to a difficulty in PWS in setting appropriate attentional weights to enable task set reconfiguration In addition to this, PWS individuals did not show the typical pattern of deactivation, with significantly less deactivation in an anterior region of the ventromedial prefrontal cortex One plausible explanation for this is that individuals with PWS show dysfunction within the default mode network which has been linked to attentional control The data point to functional changes in the neural circuitry supporting task switching in PWS even when behavioural performance is matched to controls and thus highlight neural mechanisms that may be involved in a specific pathway between genes cognition and behaviour (C) 2010 Elsevier B V All rights reserved

A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease

This work investigates the problem of feature selection in neuroimaging features from structural MRI brain images for the classification of subjects as healthy controls, suffering from Mild Cognitive Impairment or Alzheimer’s Disease. A Genetic Algorithm wrapper method for feature selection is adopted in conjunction with a Support Vector Machine classifier. In very large feature sets, feature selection is found to be redundant as the accuracy is often worsened when compared to an Support Vector Machine with no feature selection. However, when just the hippocampal subfields are used, feature selection shows a significant improvement of the classification accuracy. Three-class Support Vector Machines and two-class Support Vector Machines combined with weighted voting are also compared with the former and found more useful. The highest accuracy achieved at classifying the test data was 65.5% using a genetic algorithm for feature selection with a three-class Support Vector Machine classifier.

Optimizing optimum-path forest classification for huge datasets

Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain. © 2010 IEEE.