267 research outputs found

    Development and characterization of techniques for neuro-imaging registration

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    Three automated techniques were developed for the alignment of Neuro-Images acquired during distinct scanning periods and their performance were characterized. The techniques are based on the assumption that the human brain is a rigid body and will assume different positions during different scanning periods. One technique uses three fiducial markers, while the other two uses eigenvectors of the inertia matrix of the Neuro-Image, to compute the three angles (pitch, yaw and roll) needed to register the test Neuro-Image to the reference Neuro-Image. A rigid body transformation is computed and applied to the test Neuro-Image such that it results aligned to the reference Neuro-Image. These techniques were tested by applying known rigid body transformations to given Neuro-Images. The transformations were retrieved automatically on the basis of unit vectors or eigenvectors. The results show that the precision of two techniques is dependent on the axial resolution of the Neuro-Images and for one of them also on the imaging modality, while the precision of one technique is also dependent on the interpolation. Such methods can be applied to any Neuro-Imaging modality and have been tested for both fMRI and MRI

    Computer-aided segmentation and estimation of indices in brain CT scans

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    The importance of neuro-imaging as one of the biomarkers for diagnosis and prognosis of pathologies and traumatic cases is well established. Doctors routinely perform linear measurements on neuro-images to ascertain severity and extent of the pathology or trauma from significant anatomical changes. However, it is a tedious and time consuming process and manually assessing and reporting on large volume of data is fraught with errors and variation. In this paper we present a novel technique for segmentation of significant anatomical landmarks using artificial neural networks and estimation of various ratios and indices performed on brain CT scans. The proposed method is efficient and robust in detecting and measuring sizes of anatomical structures on non-contrast CT scans and has been evaluated on images from subjects with ages between 5 to 85 years. Results show that our method has average ICC of ≥0.97 and, hence, can be used in processing data for further use in research and clinical environment

    Teaching video neuro images. the beevor sign in late-onset pompe disease

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    The Beevor sign, an upward deflection of the umbilicus on flexion of the neck, is a characteristic finding in facioscapulohumeral muscular dystrophy.1 Many other neuromuscular disorders involving axial muscles can present a Beevor sign.2 We report a 45-year-old man with late-onset Pompe disease showing a major Beevor sign (figure 1 and video on the Neurology® Web site at Neurology.org). He had progressive limb-girdle weakness that started in his 20s and severe axial weakness. Whole-body muscle MRI showed a complete fatty replacement and atrophy of the lower part of rectus abdominis and a milder involvement of the upper par

    Teaching Neuro Images: Nonfluent variant primary progressive aphasia: A distinctive clinico-Anatomical syndrome

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    A 66-year-old woman presented with 4 years of progressive speech difficulty. She had nonfluent speech with phonemic errors but intact single-word comprehension and object knowledge. Her grammar was impaired in both speech and writing, and she exhibited orofacial apraxia. A clinico-radiologic (see figure) diagnosis of nonfluent variant primary progressive aphasia was made

    The impact of skull bone intensity on the quality of compressed CT neuro images

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    International audienceThe increasing use of technologies such as CT and MRI, along with a continuing improvement in their resolution, has contributed to the explosive growth of digital image data being generated. Medical communities around the world have recognized the need for efficient storage, transmission and display of medical images. For example, the Canadian Association of Radiologists (CAR) has recommended compression ratios for various modalities and anatomical regions to be employed by lossy JPEG and JPEG2000 compression in order to preserve diagnostic quality. Here we investigate the effects of the sharp skull edges present in CT neuro images on JPEG and JPEG2000 lossy compression. We conjecture that this atypical effect is caused by the sharp edges between the skull bone and the background regions as well as between the skull bone and the interior regions. These strong edges create large wavelet coefficients that consume an unnecessarily large number of bits in JPEG2000 compression because of its bitplane coding scheme, and thus result in reduced quality at the interior region, which contains most diagnostic information in the image. To validate the conjecture, we investigate a segmentation based compression algorithm based on simple thresholding and morphological operators. As expected, quality is improved in terms of PSNR as well as the structural similarity (SSIM) image quality measure, and its multiscale (MS-SSIM) and informationweighted (IW-SSIM) versions. This study not only supports our conjecture, but also provides a solution to improve the performance of JPEG and JPEG2000 compression for specific types of CT images

    To Learn or Not to Learn Features for Deformable Registration?

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    Feature-based registration has been popular with a variety of features ranging from voxel intensity to Self-Similarity Context (SSC). In this paper, we examine the question on how features learnt using various Deep Learning (DL) frameworks can be used for deformable registration and whether this feature learning is necessary or not. We investigate the use of features learned by different DL methods in the current state-of-the-art discrete registration framework and analyze its performance on 2 publicly available datasets. We draw insights into the type of DL framework useful for feature learning and the impact, if any, of the complexity of different DL models and brain parcellation methods on the performance of discrete registration. Our results indicate that the registration performance with DL features and SSC are comparable and stable across datasets whereas this does not hold for low level features.Comment: 9 pages, 4 figure

    An improved version of white matter method for correction of non-uniform intensity in MR images: application to the quantification of rates of brain atrophy in Alzheimer's disease and normal aging

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    A fully automated 3D version of the so-called white matter method for correcting intensity non-uniformity in MR T1-weighted neuro images is presented. The algorithm is an extension of the original work published previously. The major part of the extension was the development of a fully automated method for the generation of the reference points. In the design of this method, a number of measures were introduced to minimize the effects of possible inclusion of non-white matter voxels in the selection process. The correction process has been made iterative. PI drawback of this approach is an increased cost in computational time. The algorithm has been tested on T1-weighted MR images acquired from a longitudinal study involving elderly subjects and people with probable Alzheimer's disease. More quantitative measures were used for the evaluation of the algorithm's performance. Highly satisfactory correction results have been obtained for images with extensive intensity non-uniformity either present in raw data or added artificially. With intensity correction, improved accuracy in the measurement of the rate of brain atrophy in Alzheimer's patients as well as in elderly people due to normal aging has been achieved
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