Wilson`s disease: two treatment modalities. Correlations to pretreatment and posttreatment brain MRI

Brain magnetic resonance imaging (MRI) studies on Wilson`s disease (WD) show lack of correlations between neurological and neuroimaging features. Long-term follow-up reports with sequential brain MRI in patients with neurological WD comparing different modalities of treatment are scarce. Eighteen patients with neurological WD underwent pretreatment and posttreatment brain MRI scans to evaluate the range of abnormalities and the evolution along these different periods. All patients underwent at least two MRI scans at different intervals, up to 11 years after the beginning of treatment. MRI findings were correlated with clinical picture, clinical severity, duration of neurological symptoms, and treatment with two different drugs. Patients were divided into two groups according to treatment: d-penicillamine (D-P), zinc (Zn), and Zn after the onset of severe intolerance to D-P. MRI scans before treatment showed, in all patients, hypersignal intensity lesions on T2- and proton-density-weighted images bilaterally and symmetrically at basal nuclei, thalamus, brain stem, cerebellum, brain cortex, and brain white matter. The most common neurological symptoms were: dysarthria, parkinsonism, dystonia, tremor, psychiatric disturbances, dysphagia, risus sardonicus, ataxia, chorea, and athetosis. From the neurological point of view, there was no difference on the evolution between the group treated exclusively with D-P and the one treated with Zn. Analysis of MRI scans with longer intervals after the beginning of treatment depicted a trend for neuroimaging worsening, without neurological correspondence, among patients treated with Zn. Neuroimaging pattern of evolution was more favorable for the group that received exclusively D-P.

A fully-automatic caudate nucleus segmentation of brain MRI: application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder

Background Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations. Method We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure. Results We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis. Conclusion CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD.

Efficient total variation algorithm for fetal brain MRI reconstruction.

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy, Total Variation (TV)- based energies and more recently non-local means. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm or fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n2) and O(1/√ε), while existing techniques are in O(1/n2) and O(1/√ε). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.

An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization.

Although fetal anatomy can be adequately viewed in new multi-slice MR images, many critical limitations remain for quantitative data analysis. To this end, several research groups have recently developed advanced image processing methods, often denoted by super-resolution (SR) techniques, to reconstruct from a set of clinical low-resolution (LR) images, a high-resolution (HR) motion-free volume. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has been quite attracted by Total Variation energies because of their ability in edge preserving but only standard explicit steepest gradient techniques have been applied for optimization. In a preliminary work, it has been shown that novel fast convex optimization techniques could be successfully applied to design an efficient Total Variation optimization algorithm for the super-resolution problem. In this work, two major contributions are presented. Firstly, we will briefly review the Bayesian and Variational dual formulations of current state-of-the-art methods dedicated to fetal MRI reconstruction. Secondly, we present an extensive quantitative evaluation of our SR algorithm previously introduced on both simulated fetal and real clinical data (with both normal and pathological subjects). Specifically, we study the robustness of regularization terms in front of residual registration errors and we also present a novel strategy for automatically select the weight of the regularization as regards the data fidelity term. Our results show that our TV implementation is highly robust in front of motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI recovery as in comparison with state-of-the art methods.

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

A semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the pre- operative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These seg- mented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming proc- ess and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of a medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radi- ologist.

K-Surfer: a KNIME extension for the management and analysis of Human brain MRI FreeSurfer/FSL data

Human brain imaging techniques, such as Magnetic Resonance Imaging (MRI) or Diffusion Tensor Imaging (DTI), have been established as scientific and diagnostic tools and their adoption is growing in popularity. Statistical methods, machine learning and data mining algorithms have successfully been adopted to extract predictive and descriptive models from neuroimage data. However, the knowledge discovery process typically requires also the adoption of pre-processing, post-processing and visualisation techniques in complex data workflows. Currently, a main problem for the integrated preprocessing and mining of MRI data is the lack of comprehensive platforms able to avoid the manual invocation of preprocessing and mining tools, that yields to an error-prone and inefficient process. In this work we present K-Surfer, a novel plug-in of the Konstanz Information Miner (KNIME) workbench, that automatizes the preprocessing of brain images and leverages the mining capabilities of KNIME in an integrated way. K-Surfer supports the importing, filtering, merging and pre-processing of neuroimage data from FreeSurfer, a tool for human brain MRI feature extraction and interpretation. K-Surfer automatizes the steps for importing FreeSurfer data, reducing time costs, eliminating human errors and enabling the design of complex analytics workflow for neuroimage data by leveraging the rich functionalities available in the KNIME workbench.

Large national series of patients with Xq28 duplication involving MECP2: Delineation of brain MRI abnormalities in 30 affected patients.

Xq28 duplications encompassing MECP2 have been described in male patients with a severe neurodevelopmental disorder associated with hypotonia and spasticity, severe learning disability, stereotyped movements, and recurrent pulmonary infections. We report on standardized brain magnetic resonance imaging (MRI) data of 30 affected patients carrying an Xq28 duplication involving MECP2 of various sizes (228 kb to 11.7 Mb). The aim of this study was to seek recurrent malformations and attempt to determine whether variations in imaging features could be explained by differences in the size of the duplications. We showed that 93% of patients had brain MRI abnormalities such as corpus callosum abnormalities (n = 20), reduced volume of the white matter (WM) (n = 12), ventricular dilatation (n = 9), abnormal increased hyperintensities on T2-weighted images involving posterior periventricular WM (n = 6), and vermis hypoplasia (n = 5). The occipitofrontal circumference varied considerably between >+2SD in five patients and <-2SD in four patients. Among the nine patients with dilatation of the lateral ventricles, six had a duplication involving L1CAM. The only patient harboring bilateral posterior subependymal nodular heterotopia also carried an FLNA gene duplication. We could not demonstrate a correlation between periventricular WM hyperintensities/delayed myelination and duplication of the IKBKG gene. We thus conclude that patients with an Xq28 duplication involving MECP2 share some similar but non-specific brain abnormalities. These imaging features, therefore, could not constitute a diagnostic clue. The genotype-phenotype correlation failed to demonstrate a relationship between the presence of nodular heterotopia, ventricular dilatation, WM abnormalities, and the presence of FLNA, L1CAM, or IKBKG, respectively, in the duplicated segment.

Effects of MRI scan acceleration on brain volume measurement consistency.

PURPOSE: To evaluate the effects of recent advances in magnetic resonance imaging (MRI) radiofrequency (RF) coil and parallel imaging technology on brain volume measurement consistency. MATERIALS AND METHODS: In all, 103 whole-brain MRI volumes were acquired at a clinical 3T MRI, equipped with a 12- and 32-channel head coil, using the T1-weighted protocol as employed in the Alzheimer's Disease Neuroimaging Initiative study with parallel imaging accelerations ranging from 1 to 5. An experienced reader performed qualitative ratings of the images. For quantitative analysis, differences in composite width (CW, a measure of image similarity) and boundary shift integral (BSI, a measure of whole-brain atrophy) were calculated. RESULTS: Intra- and intersession comparisons of CW and BSI measures from scans with equal acceleration demonstrated excellent scan-rescan accuracy, even at the highest acceleration applied. Pairs-of-scans acquired with different accelerations exhibited poor scan-rescan consistency only when differences in the acceleration factor were maximized. A change in the coil hardware between compared scans was found to bias the BSI measure. CONCLUSION: The most important findings are that the accelerated acquisitions appear to be compatible with the assessment of high-quality quantitative information and that for highest scan-rescan accuracy in serial scans the acquisition protocol should be kept as consistent as possible over time. J. Magn. Reson. Imaging 2012;36:1234-1240. ©2012 Wiley Periodicals, Inc.

Automatic Brain Extraction in Fetal MRI using Multi-Atlas-based Segmentation

In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality.

MRI of the Fetal Brain.

The purpose of this article is to provide an overview of the possibilities for fetal magnetic resonance imaging (MRI) in the evaluation of the fetal brain. For brain pathologies, fetal MRI is usually performed when an abnormality is detected by previous prenatal ultrasound, and is, therefore, an important adjunct to ultrasound. The most commonly suspected brain pathologies referred to fetal MRI for further evaluation are ventriculomegaly, missing corpus callosum, and abnormalities of the posterior fossa. We will briefly discuss the most common indications for fetal brain MRI, as well as recent advances.

Brain structure in asymptomatic FMR1 premutation carriers at risk for fragile X-associated tremor/ataxia syndrome.

Fragile X-associated tremor/ataxia syndrome (FXTAS), a late-onset movement disorder affecting FMR1 premutation carriers, is associated with cerebral and cerebellar lesions. The aim of this study was to test whether computational anatomy can detect similar patterns in asymptomatic FMR1 premutation carriers (mean age 46.7 years) with qualitatively normal -appearing grey and white matter on brain MRI. We used a multimodal imaging protocol to characterize brain anatomy by automated assessment of gray matter volume and white matter properties. Structural changes in the hippocampus and in the cerebellar motor network with decreased gray matter volume in lobule VI and white matter alterations of the corresponding afferent projections through the middle cerebellar peduncles are demonstrated. Diffuse subcortical white matter changes in both hemispheres, without corresponding gray matter alterations, are only identified through age × group interactions. We interpret the hippocampal fimbria and cerebellar changes as early alterations with a possible neurodevelopmental origin. In contrast, progression of the diffuse cerebral hemispheric white matter changes suggests a neurodegenerative process, leading to late-onset lesions, which may mark the imminent onset of FXTAS.

An initial MRI picture of limbic encephalitis in subacute sclerosing panencephalitis.

BACKGROUND: Subacute sclerosing panencephalitis (SSPE) is a rare and severe long-term complication of measles. Hallmarks of this entity include progressive cognitive decline, myoclonia, a generalized periodic pattern on EEG and deep white matter abnormalities on MRI. However, imaging can be normal in early stages. AIM: We report herein the case of a previously healthy 13-years-old girl with an unusual radiological presentation. RESULTS: She presented with unilateral myoclonia, cognitive decline with memory impairment and a first brain MRI with swelling of both hippocampi mimicking limbic encephalitis. Measles antibodies were positive in CSF and the EEG showed slow periodic complexes. CONCLUSION: This unusual radiological presentation has never been described in SSPE. Relationship between virus and limbic system are discussed.

A pipeline approach with spatial information for segmenting multiple sclerosis lesions on brain magnetic resonance imaging

Background: Conventional magnetic resonance imaging (MRI) techniques are highly sensitive to detect multiple sclerosis (MS) plaques, enabling a quantitative assessment of inflammatory activity and lesion load. In quantitative analyses of focal lesions, manual or semi-automated segmentations have been widely used to compute the total number of lesions and the total lesion volume. These techniques, however, are both challenging and time-consuming, being also prone to intra-observer and inter-observer variability.Aim: To develop an automated approach to segment brain tissues and MS lesions from brain MRI images. The goal is to reduce the user interaction and to provide an objective tool that eliminates the inter- and intra-observer variability.Methods: Based on the recent methods developed by Souplet et al. and de Boer et al., we propose a novel pipeline which includes the following steps: bias correction, skull stripping, atlas registration, tissue classification, and lesion segmentation. After the initial pre-processing steps, a MRI scan is automatically segmented into 4 classes: white matter (WM), grey matter (GM), cerebrospinal fluid (CSF) and partial volume. An expectation maximisation method which fits a multivariate Gaussian mixture model to T1-w, T2-w and PD-w images is used for this purpose. Based on the obtained tissue masks and using the estimated GM mean and variance, we apply an intensity threshold to the FLAIR image, which provides the lesion segmentation. With the aim of improving this initial result, spatial information coming from the neighbouring tissue labels is used to refine the final lesion segmentation.Results:The experimental evaluation was performed using real data sets of 1.5T and the corresponding ground truth annotations provided by expert radiologists. The following values were obtained: 64% of true positive (TP) fraction, 80% of false positive (FP) fraction, and an average surface distance of 7.89 mm. The results of our approach were quantitatively compared to our implementations of the works of Souplet et al. and de Boer et al., obtaining higher TP and lower FP values.Conclusion: Promising MS lesion segmentation results have been obtained in terms of TP. However, the high number of FP which is still a well-known problem of all the automated MS lesion segmentation approaches has to be improved in order to use them for the standard clinical practice. Our future work will focus on tackling this issue.

Lack of MRI neurohypophyseal bright signal in a child with congenital nephrogenic diabetes insipidus

Congenital nephrogenic diabetes insipidus (CNDI) is a rare disease characterized by the inability of the kidney to respond to arginine vasopressin (AVP). The absence of the neurohypophyseal 'bright signal' on T1 sequence magnetic resonance imaging (MRI) is considered as an argument in favour of the diagnosis of central diabetes insipidus (CDI). This observation is challenged as we hereby present a case of a child diagnosed with CNDI and who did not present MRI pituitary bright signal. A 6-month-old male presented with failure to thrive, polyuria and polydypsia. Family history revealed that the mother, 35 years of age, had been presenting polydypsia and polyuria, and she was investigated at the age of 6 years with no concluding diagnosis. The patient's physical exam showed a weight of 5215 g (−3 DS) and clinical signs of dehydration. The patient's plasma sodium level was 155 mmol/L, osmolality 305 mOsm/kg and urine osmolality 150 mOsm/kg. Brain MRI showed in T1 sequences the absence of the posterior pituitary bright signal suggesting the diagnosis of CDI (Figure 1). The child was treated with synthetic AVP analogue 1-desamino-8-d-arginine vasopressin (DDAVP) without improvement, which led to the consideration of CNDI. The diagnosis was confirmed by an elevated serum level of AVP of 214 pmol/L (reference value ≤4.34 pmol/L) and by genetic analysis demonstrating and T106C mutation in the V2R (X-linked CNDI). The child was treated with thiazide diuretic and increased fluids with restricted sodium intake. This resulted in catch-up growth and improved neurological development. A follow-up MRI was performed 6 months after the start of therapy with the same technique. At that time, the child's weight had improved to 9310 g (−1.5 DS) corresponding to a gain of 22 g per day, and he did not present any clinical signs of dehydration and had a normal plasma level of sodium (140 mmol/L). MRI showed that the bright signal was still absent.