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      Diagnostic Ability of Structural Transcranial Sonography in Patients with Alzheimer’s Disease

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          Abstract

          The aim of this study was to assess the diagnostic ability of transcranial sonography (TCS) for the evaluation of the medial temporal lobe (MTL) in Alzheimer’s disease (AD). Standard neuropsychological evaluation, TCS and 1.5 T MRI were performed for 20 patients with AD and for 20 age- and sex-matched healthy controls in a prospective manner. Measurements of the size of the third ventricle and heights of the MTL (A) and the choroidal fissure (B) were performed twice on each side by two independent neurosonologists for all participants. On MRI, both conventional and volumetric analyses of the third ventricle and hippocampus were performed. Receiver operating characteristic (ROC) curves analyses were applied. Height of the MTL on TCS had sensitivities of 73.7% (right)/63.2%(left) and specificities of 65% (right)/65–70% (left) Area under a curve (AUC) 75.4–77.2% (right), 60.4–67.8% (left)) for AD. A/B ratio on TCS had sensitivities of 73.7% (right)/57.9% (left) and specificities of 70.0% (right)/55.0% (left) (AUC 73.3% (right), 60.4% (left)) by the experienced neurosonologist, and sensitivities of 78.9% (right and left) and specificities of 60.0% (right)/65.0% (left) (AUC 77.8–80.0%) by the inexperienced neurosonologist for AD. On MRI, linear measurement of the hippocampus and parahippocampal gyrus height had sensitivities of 84.2% (right)/89.5% (left) and specificities of 80.0% (right)/85% (left) (AUC 86.1–92.9%) for AD. Hippocampal volume had sensitivities of 70% (right and left) and specificities of 75% (right)/80% (left) (AUC 77.5–78%) for AD. Atrophy of the right MTL in AD could be detected on TCS with a good diagnostic ability, however MRI performed better on the left.

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          A hybrid approach to the skull stripping problem in MRI.

          We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1-weighted MRI image, and uses it to create a global minimum in the white matter before applying a watershed algorithm with a preflooding height. The watershed algorithm builds an initial estimate of the brain volume based on the three-dimensional connectivity of the white matter. This first step is robust, and performs well in the presence of intensity nonuniformities and noise, but may erode parts of the cortex that abut bright nonbrain structures such as the eye sockets, or may remove parts of the cerebellum. To correct these inaccuracies, a surface deformation process fits a smooth surface to the masked volume, allowing the incorporation of geometric constraints into the skull-stripping procedure. A statistical atlas, generated from a set of accurately segmented brains, is used to validate and potentially correct the segmentation, and the MRI intensity values are locally re-estimated at the boundary of the brain. Finally, a high-resolution surface deformation is performed that accurately matches the outer boundary of the brain, resulting in a robust and automated procedure. Studies by our group and others outperform other publicly available skull-stripping tools. Copyright 2004 Elsevier Inc.
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            Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach.

            Abstract We describe a comprehensive linear approach to the problem of imaging brain activity with high temporal as well as spatial resolution based on combining EEG and MEG data with anatomical constraints derived from MRI images. The "inverse problem" of estimating the distribution of dipole strengths over the cortical surface is highly underdetermined, even given closely spaced EEG and MEG recordings. We have obtained much better solutions to this problem by explicitly incorporating both local cortical orientation as well as spatial covariance of sources and sensors into our formulation. An explicit polygonal model of the cortical manifold is first constructed as follows: (1) slice data in three orthogonal planes of section (needle-shaped voxels) are combined with a linear deblurring technique to make a single high-resolution 3-D image (cubic voxels), (2) the image is recursively flood-filled to determine the topology of the gray-white matter border, and (3) the resulting continuous surface is refined by relaxing it against the original 3-D gray-scale image using a deformable template method, which is also used to computationally flatten the cortex for easier viewing. The explicit solution to an error minimization formulation of an optimal inverse linear operator (for a particular cortical manifold, sensor placement, noise and prior source covariance) gives rise to a compact expression that is practically computable for hundreds of sensors and thousands of sources. The inverse solution can then be weighted for a particular (averaged) event using the sensor covariance for that event. Model studies suggest that we may be able to localize multiple cortical sources with spatial resolution as good as PET with this technique, while retaining a much finer grained picture of activity over time.
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              Volume reduction of the entorhinal cortex in subjective memory impairment.

              To examine the biological basis of subjective memory impairment (SMI), defined as the feeling of memory worsening with normal memory performance, we measured the volume of the entorhinal cortex (EC) and the hippocampus in SMI subjects, patients with mild cognitive impairment (MCI), patients with Alzheimer's disease (AD) and healthy controls (CO). Compared with controls, the EC was smaller in the SMI group (left: p=0.060; right: p=0.045) and in the other two groups in the following order: CO>SMI>MCI>AD. The same sequence was observed with regard to hippocampal volumes, but the volume reduction of the left hippocampus in the SMI group only reached a trend towards significance (p=0.072) and the right was not significantly smaller compared with controls (p=0.37). Compared with controls the average (left/right) volume reduction of the EC was 18% (SMI), 26% (MCI) and 44% (AD). The mean volume reduction of the hippocampus was 6% (SMI), 16% (MCI) and 19% (AD). Our results mirror the temporal sequence of neurodegeneration in AD and support the concept of SMI as the first clinical manifestation of dementia.
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                Author and article information

                Journal
                Diagnostics (Basel)
                Diagnostics (Basel)
                diagnostics
                Diagnostics
                MDPI
                2075-4418
                10 July 2020
                July 2020
                : 10
                : 7
                : 471
                Affiliations
                [1 ]Department of Neurology, Medical Academy, Lithuanian University of Health Sciences, A. Mickevičiaus Str. 9., LT-44307 Kaunas, Lithuania; Vaidas.Matijosaitis@ 123456lsmuni.lt (V.M.); Ovidijus.Laucius@ 123456lsmuni.lt (O.L.)
                [2 ]Department of Radiology, Medical Academy, Lithuanian University of Health Sciences, A. Mickevičiaus Str. 9., LT-44307 Kaunas, Lithuania; Rymante.Gleizniene@ 123456lsmuni.lt (R.G.); s.jesmanas@ 123456gmail.com (S.J.)
                [3 ]Institute of Cardiology, Medical Academy, Lithuanian University of Health Sciences, A. Mickevičiaus Str. 9., LT-44307 Kaunas, Lithuania; Kristina.Jureniene@ 123456lsmuni.lt
                Author notes
                Author information
                https://orcid.org/0000-0002-5603-2357
                https://orcid.org/0000-0002-5206-3033
                Article
                diagnostics-10-00471
                10.3390/diagnostics10070471
                7400014
                32664455
                2eb70dcb-b874-4dc8-bb59-df65bcf9b59b
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 10 June 2020
                : 08 July 2020
                Categories
                Article

                transcranial sonography,magnetic resonance imaging,volumetry,medial temporal lobe,alzheimer’s disease

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