Thrombosis of the great cerebral vein in a hemodialysis patient

Cerebral venous thrombosis is a rare condition with various clinical presentations which may delay diagnosis. It is frequently associated with severe consequences. We present the first documented case of thrombosis of the great cerebral vein in a hemodialysis patient. A 29‐year‐old female patient with end‐stage renal disease of unknown etiology was admitted to a hospital with altered consciousness and nausea. Severe headache in the right parietal area had started 2 days before. On examination, she was in the poor overall condition, dysartric, with a severe nystagmus. Urgent brain multislice computerized tomography and magnetic resonance imaging revealed thrombosis of the great cerebral vein with hypodense zones in hypothalamus, thalamus and basal ganglia. She was treated with heparin bolus of 25000 IU with a favorable outcome. Detailed examination demonstrated increased lupus anticoagulant (LA) 1 and LA2 and increased LA1/LA2. Control magnetic resonance imaging performed 1 year later revealed multiple vascular lesions within the brain. Acetylsalicylate was introduced in therapy. Thrombosis of the cerebral veins should be suspected in patients with end‐stage renal disease, altered neurological status and signs of increased intracranial pressure.     

Imaging manifestations of sepsis‐associated encephalopathy

The purpose of this study was to retrospectively analyze the clinical and imaging data of sepsis‐associated encephalopathy (SAE) following infantile diarrhea. Eight infants were diagnosed with SAE after diarrhea and assessed using computed tomography (CT) and magnetic resonance imaging (MRI). The main symptoms present in each of the eight patients were fever, diarrhea, seizures, and changes in consciousness. From cranial CT examination, five patients showed normal results, two displayed cerebral edema, and one displayed slightly lower density areas in the frontal and parietal lobes. There were four patients examined through MRI: one patient displayed slightly widened cerebral sulci; two displayed edema in the white cerebral matter and basal ganglia with gyriform enhancement after contrast; one displayed an abnormal bilateral signal in the occipital lobes and another in the frontoparietal lobe; and one displayed a diffuse abnormal signal in the white matter of both cerebral hemispheres and basal ganglia. Imaging manifestations of SAE included encephaledema or abnormal signals in the white matter and basal ganglia. CT examination can exclude other cerebral pathologies causing brain dysfunction in early stages. MRI examination can provide more information to aid in the early diagnosis of SAE.     

Residual UNet with Dual Attention—An ensemble residual UNet with dual attention for multi-modal and multi-class brain MRI segmentation

In this paper, we have proposed a variant of UNet for brain magnetic resonance imaging (MRI) segmentation. The proposed model, termed as Residual UNet with Dual Attention (RUDA), addresses the two significant challenges of UNet: extraction of the complex features with unclear boundaries and the problem of over-segmentation due to the redundancy caused by the skip connection usage. RUDA is constituted upon the residual blocks for extracting the complex structures. It Introduces attention into the skip connections to avoid redundancy and thereby the chance of over-segmentation. Our model segments brain MRI into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) regions, which are considered crucial informative substructures for diagnosing neurological disorders such as Alzheimer's. It has been implemented in an ensemble manner to accommodate the multi-sequence (T1-weighted, IR, and T2-FLAIR) scans. The empirical analysis shows that with an accuracy of 93.80%, RUDA outperforms the two baseline models: UNet (91.37%), ResUNet (91.44%).     

Automatic segmentation of cerebral hemispheres in MR human head scans

Automatic segmentation of cerebral hemispheres in magnetic resonance (MR) brain images help to quantify the brain asymmetry and correct several MR brain deformities. The detection of mid‐sagittal plane (MSP) in human brain image is necessary to segment the hemispheres for both operator‐based and automated brain image asymmetric analysis. In this article, a computationally simple and accurate technique to detect MSP in MRI human head scans using curve fitting is developed. The left and right hemispheres are segmented based on the detected MSP. The accuracy of the MSP is evaluated by comparing the segmented left and right hemispheres against the manually segmented ones. Experimental results using 78 volumes of T1, T2 and PD‐weighted MRI brain images show that the proposed method has accurately segmented the cerebral hemispheres based on the detected MSP in axial and coronal orientations of normal and pathological brain images.     

Two additional cases of metformin‐associated encephalopathy in patients with end‐stage renal disease undergoing hemodialysis

We report on two additional cases of metformin‐associated encephalopathy in patients with end‐stage renal disease (ESRD) undergoing hemodialysis. Two patients were seen at our hospital with abnormal neurological signs and symptoms. Magnetic resonance imaging (MRI) revealed the same pattern of high signal intensity in both basal ganglia in T2‐weighted images in the two patients. The two patients had started taking metformin 5 and 6 weeks earlier at the same dose of 1000 mg per day. Metformin was immediately stopped, and regular hemodialysis was conducted. Their signs and symptoms resolved completely after these measures. The high signal intensity in both ganglia in T2‐weighted MRI also disappeared. We should suspect metformin‐induced encephalopathy and withdraw the drug when presented with diabetic patients with chronic kidney disease and neurological signs and symptoms of unknown cause.     

Diffuse optical tomography system to image brain activation with improved spatial resolution and validation with functional magnetic resonance imaging

Although most current diffuse optical brain imaging systems use only nearest- neighbor measurement geometry, the spatial resolution and quantitative accuracy of the imaging can be improved through the collection of overlapping sets of measurements. A continuous-wave diffuse optical imaging system that combines frequency encoding with time-division multiplexing to facilitate overlapping measurements of brain activation is described. Phantom measurements to confirm the expected improvement in spatial resolution and quantitative accuracy are presented. Experimental results showing the application of this instrument for imaging human brain activation are also presented. The observed improvement in spatial resolution is confirmed by functional magnetic resonance imaging.     

Monte Carlo prediction of near-infrared light propagation in realistic adult and neonatal head models

In near-infrared spectroscopy and imaging, the sensitivity of the detected signal to brain activation and the volume of interrogated tissue are clinically important. Light propagation in adult and neonatal heads is strongly affected by the presence of a low-scattering cerebrospinal fluid layer. The effect of the heterogeneous structure of the head on light propagation in the adult brain is likely to be different from that in the neonatal brain because the thickness of the superficial tissues and the optical properties of the brain of the neonatal head are quite different from those of the adult head. In this study, light propagation in the two-dimensional realistic adult and neonatal head models, whose geometries are generated from a magnetic resonance imaging scan of the human heads, is predicted by Monte Carlo simulation. The sandwich structure, which is a low-scattering cerebrospinal fluid layer held between the high-scattering skull and gray matter, strongly affects light propagation in the brain of the adult head. The sensitivity of the absorption change in the gray matter is improved; however, the intensely sensitive region is confined to the shallow region of the gray matter. The high absorption of the neonatal brain causes a similar effect on light propagation in the head. The intensely sensitive region in the neonatal brain is confined to the gray matter; however, the spatial sensitivity profile penetrates into the deeper region of the white matter.     

A geometric flow-based approach for diffusion tensor image segmentation

Diffusion tensor magnetic resonance imaging (DT-MRI, shortened as DTI) produces, from a set of diffusion-weighted magnetic resonance images, tensor-valued images where each voxel is assigned a 3x3 symmetric, positive-definite matrix. This tensor is simply the covariance matrix of a local Gaussian process with zero mean, modelling the average motion of water molecules. We propose a three-dimensional geometric flow-based model to segment the main core of cerebral white matter fibre tracts from DTI. The segmentation is carried out with a front propagation algorithm. The front is a three-dimensional surface that evolves along its normal direction with speed that is proportional to a linear combination of two measures: a similarity measure and a consistency measure. The similarity measure computes the similarity of the diffusion tensors at a voxel and its neighbouring voxels along the normal to the front; the consistency measure is able to speed up the propagation at locations where the evolving front is more consistent with the diffusion tensor field, to remove noise effect to some extent, and thus to improve results. We validate the proposed model and compare it with some other methods using synthetic and human brain DTI data; experimental results indicate that the proposed model improves the accuracy and efficiency in segmentation.     

A comprehensive review: Segmentation of MRI images—brain tumor

Segmentation of tumors in human brain aims to classify different abnormal tissues (necrotic core, edema, active cells) from normal tissues (cerebrospinal fluid, gray matter, white matter) of the brain. In existence, detection of abnormal tissues is easy for studying brain tumor, but reproducibility, characterization of abnormalities and accuracy are complicated in the process of segmentation. The magnetic resonance imaging (MRI)‐based segmentation of tumors in brain images is more enhancing and attracting in current years of research studies. It is due to non‐invasive examination and good contrast prone to soft tissues of images obtained from MRI modality. Medical approval of different segmentation techniques depends on the benchmark and simplicity of the method. This article incorporates both fully‐automatic and semi‐automatic methods for segmentation. The outlook study of this article is to provide the summary of most significant segmentation methods of tumors in brain using MRI. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 295–304, 2016     

Fully automatic method for segmentation of brain tumor from multimodal magnetic resonance images using wavelet transformation and clustering technique

Fully automatic brain tumor segmentation is one of the critical tasks in magnetic resonance imaging (MRI) images. This proposed work is aimed to develop an automatic method for brain tumor segmentation process by wavelet transformation and clustering technique. The proposed method using discrete wavelet transform (DWT) for pre‐ and post‐processing, fuzzy c‐means (FCM) for brain tissues segmentation. Initially, MRI images are preprocessed by DWT to sharpen the images and enhance the tumor region. It assists to quicken the FCM clustering technique and classified into four major classes: gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and background (BG). Then check the abnormality detection using Fuzzy symmetric measure for GM, WM, and CSF classes. Finally, DWT method is applied in segmented abnormal region of images respectively and extracts the tumor portion. The proposed method used 30 multimodal MRI training datasets from BraTS2012 database. Several quantitative measures were calculated and compared with the existing. The proposed method yielded the mean value of similarity index as 0.73 for complete tumor, 0.53 for core tumor, and 0.35 for enhancing tumor. The proposed method gives better results than the existing challenging methods over the publicly available training dataset from MICCAI multimodal brain tumor segmentation challenge and a minimum processing time for tumor segmentation. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 305–314, 2016     

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Magnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer-assisted diagnosis tool. The fused Bag-of-Features (BoF) with Speeded-Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1-weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs. The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion.     

A multi‐modal,multi‐atlas‐based approach for Alzheimer detection via machine learning

The use of biomarkers for early detection of Alzheimer's disease (AD) improves the accuracy of imaging‐based prediction of AD and its prodromal stage that is mild cognitive impairment (MCI). Brain parcellation‐based computer‐aided methods for detecting AD and MCI segregate the brain in different anatomical regions and use their features to predict AD and MCI. Brain parcellation generally is carried out based on existing anatomical atlas templates, which vary in the boundaries and number of anatomical regions. This works considers dividing the brain based on different atlases and combining the features extracted from these anatomical parcellations for a more holistic and robust representation. We collected data from the ADNI database and divided brains based on two well‐known atlases: LONI Probabilistic Brain Atlas (LPBA40) and Automated Anatomical Labeling (AAL). We used baselines images of structural magnetic resonance imaging (MRI) and ¹⁸F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) to calculate average gray‐matter density and average relative cerebral metabolic rate for glucose in each region. Later, we classified AD, MCI and cognitively normal (CN) subjects using the individual features extracted from each atlas template and the combined features of both atlases. We reduced the dimensionality of individual and combined features using principal component analysis, and used support vector machines for classification. We also ranked features mostly involved in classification to determine the importance of brain regions for accurately classifying the subjects. Results demonstrated that features calculated from multiple atlases lead to improved performance compared to those extracted from one atlas only.     

Simulation study of magnetic resonance imaging-guided cortically constrained diffuse optical tomography of human brain function

Diffuse optical imaging can measure brain activity noninvasively in humans through the scalp and skull by measuring the light intensity modulation arising from localized-activity-induced absorption changes within the cortex. Spatial resolution and localization accuracy are currently limited by measurement geometry to approximately 3 cm in the plane parallel to the scalp. Depth resolution is a more significant challenge owing to the limited angle tomography permitted by reflectance-only measurements. We combine previously established concepts for improving image quality and demonstrate, through simulation studies, their application for improving the image quality of adult human brain function. We show in a three-dimensional human head model that localization accuracy is significantly improved by the addition of measurements that provide overlapping samples of brain tissue. However, the reconstructed absorption contrast is significantly underestimated because its depth is underestimated. We show that the absorption contrast amplitude accuracy can be significantly improved by providing a cortical spatial constraint in the image reconstruction to obtain a better depth localization. The cortical constraint makes physiological sense since the brain-activity-induced absorption changes are occurring in the cortex and not in the scalp, skull, and cerebral spinal fluid. This spatial constraint is provided by segmentation of coregistered structural magnetic resonance imaging (MRI). However, the absorption contrast deep within the cortex is reconstructed superficially, resulting in an underestimation of the absorption contrast. The synthesis of techniques described here indicates that multimodality imaging of brain function with diffuse optical imaging and MRI has the potential to provide more quantitative estimates of the total and deoxyhemoglobin response to brain activation, which is currently not provided by either method independently. However, issues of depth resolution within the cortex remain to be resolved.     

When it rains,it pours: Peripartum cardiomyopathy with features of left‐ventricular noncompaction in a hemodialysis patient

Pregnancy and associated pre‐eclampsia carry a high maternal risk in hemodialysis patients, yet no guidelines on how to monitor these patients' cardiovascular function exist. A 34‐year‐old hemodialysis patient presented with peripartum cardiomyopathy after a late second trimester miscarriage. On cardiac magnetic resonance imaging, diagnostic features of left ventricular noncompaction were apparent. Yet, histological and gene panel analyses remained negative. Upon stringent dry weight control and pharmacological heart failure therapy, the pathological changes showed complete regression. As pregnant hemodialysis patients have an excessively increased risk for pre‐eclampsia‐related cardiac disease, thorough screening appears valuable in these patients.     

Neurofeedback using functional spectroscopy

Neurofeedback based on real‐time measurement of the blood oxygenation level‐dependent (BOLD) signal has potential for treatment of neurological disorders and behavioral enhancement. Commonly used methods are based on functional magnetic resonance imaging (fMRI) sequences that sacrifice speed and accuracy for whole‐brain coverage, which is unnecessary in most applications. We present multivoxel functional spectroscopy (MVFS): a system for computing the BOLD signal from multiple volumes of interest (VOI) in real‐time that improves speed and accuracy of neurofeedback. MVFS consists of a FS pulse sequence, a BOLD reconstruction component, a neural activation estimator, and a stimulus system. The FS pulse sequence is a single‐voxel, magnetic resonance spectroscopy sequence without water suppression that has been extended to allow acquisition of a different VOI at each repetition and real‐time subject head motion compensation. The BOLD reconstruction component determines the T2* decay rate, which is directly related to BOLD signal strength. The neural activation estimator discounts nuisance signals and scales the activation relative to the amount of ROI noise. Finally, the neurofeedback system presents neural activation‐dependent stimuli to experimental subjects with an overall delay of less than 1 s. Here, we present the MVFS system, validation of certain components, examples of its usage in a practical application, and a direct comparison of FS and echo‐planar imaging BOLD measurements. We conclude that in the context of realtime BOLD imaging, MVFS can provide superior accuracy and temporal resolution compared with standard fMRI methods. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 138–148, 2014     

Metformin-induced encephalopathy without lactic acidosis in a patient with contraindication for metformin

A 51-year-old Korean man with end-stage renal disease and who was on intermittent hemodialysis was admitted with progressive dysarthria, gait disturbance, and myoclonus. The liver function tests and the electrolyte and arterial gas analyses were normal. The Magnetic resonance imaging scan showed a diffuse symmetric high signal intensity in the basal ganglia on the T2-weighted image. After a thorough history taking, we knew that he had been treated with metformin for 3 months at other hospital without our hospitals staff's knowledge. After stopping the metformin, the patient's neurologic signs and symptoms disappeared and the Magnetic resonance imaging findings after 20 days were markedly improved.     

Fully automatic brain tumor extraction and tissue segmentation from multimodal MRI brain images

Segmentation of Brain tumor from the magnetic resonance imaging (MRI) of head scans is an essential requirement for clinical diagnosis since manual segmentation is a fatigue and time‐consuming process. Recent computer‐aided diagnosis systems depend on the development of fully automatic methods to overcome these problems. In the present work, a fully automated algorithm is proposed to extract and segment tumor regions from multimodal magnetic resonance imaging (MMMRI) sequences. The algorithm has three phases: (a) tumor portion extraction, (b) tumor substructure segmentation, and (c) 3D postprocessing. First, the algorithm extracts tumor portion using a set of image processing operations from T2, fluid‐attenuated inversion recovery (FLAIR), and T1C images. Here, the proposed modified fuzzy c means clustering algorithm is used for enhancing the tumor portion extraction process. Then, the substructures of tumor such as edema, enhancing tumor, and necrotic regions are segmented from MMMRI sequences, T2, FLAIR, and T1C using region‐wise set operations in Phase II. Finally, 3D visualization of the segmented tumor and volume estimation is performed as postprocessing in Phase III. The proposed work was experimented on BraTS 2013 dataset. The quantitative analysis is performed using William's Index, Dice, sensitivity, specificity, and accuracy and is compared with 19 state‐of‐the‐art methods. The proposed method yields comparable results as 77%, 53%, and 59% of Dice for complete, core, and enhancing tumor regions, respectively.     

In vivo imaging and biodistribution of multimodal polymeric nanoparticles delivered to the optic nerve

The use of nanoparticles for targeted delivery of therapeutic agents to sites of injury or disease in the central nervous system (CNS) holds great promise. However, the biodistribution of nanoparticles following in vivo administration is often unknown, and concerns have been raised regarding potential toxicity. Using poly(glycidyl methacrylate) (PGMA) nanoparticles coated with polyethylenimine (PEI) and containing superparamagnetic iron oxide nanoparticles as a magnetic resonance imaging (MRI) contrast agent and rhodamine B as a fluorophore, whole animal MRI and fluorescence analyses are used to demonstrate that these nanoparticles (NP) remain close to the site of injection into a partial injury of the optic nerve, a CNS white matter tract. In addition, some of these NP enter axons and are transported to parent neuronal somata. NP also remain in the eye following intravitreal injection, a non-injury model. Considerable infiltration of activated microglia/macrophages occurs in both models. Using magnetic concentration and fluorescence visualization of tissue homogenates, no dissemination of the NP into peripheral tissues is observed. Histopathological analysis reveals no toxicity in organs other than at the injection sites. Multifunctional nanoparticles may be a useful mechanism to deliver therapeutic agents to the injury site and somata of injured CNS neurons and thus may be of therapeutic value following brain or spinal cord trauma.     

High-resolution tomographic imaging of a human cerebellum: comparison of absorption and grating-based phase contrast

Human brain tissue belongs to the most impressive and delicate three-dimensional structures in nature. Its outstanding functional importance in the organism implies a strong need for brain imaging modalities. Although magnetic resonance imaging provides deep insights, its spatial resolution is insufficient to study the structure on the level of individual cells. Therefore, our knowledge of brain microstructure currently relies on two-dimensional techniques, optical and electron microscopy, which generally require severe preparation procedures including sectioning and staining. X-ray absorption microtomography yields the necessary spatial resolution, but since the composition of the different types of brain tissue is similar, the images show only marginal contrast. An alternative to absorption could be X-ray phase contrast, which is known for much better discrimination of soft tissues but requires more intricate machinery. In the present communication, we report an evaluation of the recently developed X-ray grating interferometry technique, applied to obtain phase-contrast as well as absorption-contrast synchrotron radiation-based microtomography of human cerebellum. The results are quantitatively compared with synchrotron radiation-based microtomography in optimized absorption-contrast mode. It is demonstrated that grating interferometry allows identifying besides the blood vessels, the stratum moleculare, the stratum granulosum and the white matter. Along the periphery of the stratum granulosum, we have detected microstructures about 40 µm in diameter, which we associate with the Purkinje cells because of their location, size, shape and density. The detection of individual Purkinje cells without the application of any stain or contrast agent is unique in the field of computed tomography and sets new standards in non-destructive three-dimensional imaging.