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White matter hyperintensities are more highly associated with preclinical Alzheimer's disease than imaging and cognitive markers of neurodegeneration

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Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring 2016年 4卷 18-27页

作者： Kandel, Benjamin M. Avants, Brian B. Gee, James C. McMillan, Corey T. Erus, Guray Doshi, Jimit Davatzikos, Christos Wolk, David A. Penn Image Computing and Science Laboratory and Department of Bioengineering University of Pennsylvania Philadelphia PA United States Penn Image Computing and Science Laboratory and Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Neurology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA United States

Introduction: Cognitive tests and nonamyloid imaging biomarkers do not consistently identify preclinical AD. The objective of this study was to evaluate whether white matter hyperintensity (WMH) volume, a cerebrovascular disease marker, is more associated with preclinical AD than conventional AD biomarkers and cognitive tests. Methods: Elderly controls enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 158) underwent florbetapir-PET scans, psychometric testing, neuroimaging with MRI and PET, and APOE genetic testing. Elderly controls the Parkinson's progression markers initiative (PPMI, n = 58) had WMH volume, cerebrospinal fluid (CSF) Aβ_1-42, and APOE status measured. Results: In the ADNI cohort, only WMH volume and APOE ε4 status were associated with cerebral Aβ (standardized β = 0.44 and 1.25, P = .03 and .002). The association between WMH volume and APOE ε4 status with cerebral Aβ (standardized β = 1.12 and 0.26, P = .048 and .045) was confirmed in the PPMI cohort. Discussion: WMH volume is more highly associated with preclinical AD than other AD biomarkers. © 2016 The Authors.

关键词： Aging Alzheimer's disease Amyloid Leukoaraiosis MRI PET Preclinical Alzheimer's disease White matter

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Tensor-Based Morphometry of Fibrous Structures with Application to Human Brain White Matter

Tensor-Based Morphometry of Fibrous Structures with Applicat...

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12th International Conference on Medical image computing and Computer-Assisted Intervention (MICCAI2009)

作者： Zhang, Hui Yushkevich, Paul A. Rueckert, Daniel Gee, James C. Penn Image Computing and Science Laboratory (PICSL) Department of Radiology University of Pennsylvania United States Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783642042706

Tensor-based morphometry (TBM) is a powerful approach for examining shape changes in anatomy both across populations and in time. Our work extends the standard TBM for quantifying local volumetric changes to establish both rich and intuitive descriptors of shape changes in fibrous structures. It leverages the data from diffusion tensor imaging to determine local spatial configuration of fibrous structures and combines this information with spatial transformations derived from image registration to quantify fibrous structure-specific changes, such as local changes in fiber length and in thickness of fiber bundles. In this paper, we describe the theoretical framework of our approach in detail and illustrate its application to study brain white matter. Our results show that additional insights can be gained with the proposed analysis. © 2009 Springer-Verlag.

关键词： Diffusion tensor imaging

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Spatial bias in multi-atlas based segmentation

Spatial bias in multi-atlas based segmentation

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hongzhi Wang Paul A. Yushkevich Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania USA

Multi-atlas segmentation has been widely applied in medical image analysis. With deformable registration, this technique realizes label transfer from pre-labeled atlases to unknown images. When deformable registration produces error, label fusion that combines results produced by multiple atlases is an effective way for reducing segmentation errors. Among the existing label fusion strategies, similarity-weighted voting strategies with spatially varying weight distributions have been particularly successful. We show that, weighted voting based label fusion produces a spatial bias that under-segments structures with convex shapes. The bias can be approximated as applying spatial convolution to the ground truth spatial label probability maps, where the convolution kernel combines the distribution of residual registration errors and the function producing similarity-based voting weights. To reduce this bias, we apply a standard spatial deconvolution to the spatial probability maps obtained from weighted voting. In a brain image segmentation experiment, we demonstrate the spatial bias and show that our technique substantially reduces this spatial bias.

关键词： image segmentation Kernel Convolution Deconvolution Hippocampus Accuracy Magnetic resonance imaging

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Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum

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Translational Neurodegeneration 2023年第1期12卷 50-69页

作者： Ting Shen Jacob W.Vogel Jeffrey Duda Jeffrey S.Phillips Philip ACook James Gee Lauren Elman Colin Quinn Defne A.Amado Michael Baer Lauren Massimo Murray Grossman David J.Irwin Corey T.McMillan Penn Frontotemporal Degeneration Center Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPA 19104USA Department of Clinical Sciences SciLifeLabLund University22242 LundSweden Penn Image Computing and Science Lab(PICSL) Department of RadiologyPerelman School of MedicineUniversity of Penn-sylvaniaPhiladelphiaPA 19104USA Department of Neurology Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPA 19104USA Digital Neuropathology Laboratory Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPA 19104USA.

Background TDP-43 proteinopathies represent a spectrum of neurological disorders,anchored clinically on either end by amyotrophic lateral sclerosis(ALS)and frontotemporal degeneration(FTD).The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes,highlighting its *** study was aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD *** We used a data-driven procedure to identify 13 anatomic clusters of brain volume for 57 behavioral variant FTD(bvFTD;with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants),103 ALS,and 47 ALS-FTD patients with likely TDP-43.A Subtype and Stage Inference(SuStaIn)model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns,and we related subtypes and stages to clinical,genetic,and neuropathological features of *** SuStaIn identified three novel subtypes:two disease subtypes with predominant brain atrophy in either prefrontal/somatomotor regions or limbic-related regions,and a normal-appearing group without obvious brain *** limbic-predominant subtype tended to present with more impaired cognition,higher frequencies of pathogenic variants in TBK1 and TARDBP genes,and a higher proportion of TDP-43 types B,E and *** contrast,the prefrontal/somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type *** normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients,higher cognitive capacity,higher proportion of lower motor neuron onset,milder motor symptoms,and lower frequencies of genetic pathogenic *** overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration,higher King’s stage,and cog

关键词： Amyotrophic lateral sclerosis Frontotemporal degeneration Disease heterogeneity SuStaIn model

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Domain Generalizer: A few-shot meta learning framework for domain generalization in medical imaging

arXiv

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arXiv 2020年

作者： Khandelwal, Pulkit Yushkevich, Paul Department of Bioengineering University of Pennsylvania PhiladelphiaPA United States Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania PhiladelphiaPA United States

Deep learning models perform best when tested on target (test) data domains whose distribution is similar to the set of source (train) domains. However, model generalization can be hindered when there is significant difference in the underlying statistics between the target and source domains. In this work, we adapt a domain generalization method based on a model-agnostic meta-learning framework [1] to biomedical imaging. The method learns a domain-agnostic feature representation to improve generalization of models to the unseen test distribution. The method can be used for any imaging task, as it does not depend on the underlying model architecture. We validate the approach through a computed tomography (CT) vertebrae segmentation task across healthy and pathological cases on three datasets. Next, we employ few-shot learning, i.e. training the generalized model using very few examples from the unseen domain, to quickly adapt the model to new unseen data distribution. Our results suggest that the method could help generalize models across different medical centers, image acquisition protocols, anatomies, different regions in a given scan, healthy and diseased populations across varied imaging modalities. Copyright © 2020, The Authors. All rights reserved.

关键词： Computerized tomography

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Reconstruction of the human hippocampus in 3D from histology and high-resolution ex-vivo MRI

Reconstruction of the human hippocampus in 3D from histology...

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IEEE International Symposium on Biomedical Imaging

作者： Daniel H. Adler Alex Yang Liu John Pluta Salmon Kadivar Sylvia Orozco Hongzhi Wang James C. Gee Brian B. Avants Paul A. Yushkevich Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania Philadelphia PA USA

In this paper, we present methods for the reconstruction of 3D histological volumes of the human hippocampal formation from histology slices. Inter-slice alignment is guided by a graph-theoretic approach that minimizes the impact of badly distorted slices. The reconstruction is refined by iterative affine and deformable co-registration with a high-resolution MRI of the postmortem tissue sample. We present an evaluation of reconstruction accuracy that is based on measures of similarity between boundaries drawn on both histology and MRI. Our methodology is currently being applied to an MRI atlas of the human hippocampal formation, in which atlas anatomical labels are derived from segmentation of reconstructed histology.

关键词： Magnetic resonance imaging image reconstruction Hippocampus Humans Stacking Hafnium Shape

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"Nonparametric Local Smoothing" is not image registration

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BMC Research Notes 2012年第1期5卷 1-5页

作者： Rohlfing, Torsten Avants, Brian Neuroscience Program SRI International Menlo Park CA 94025 333 Ravenswood Avenue United States Penn Image Computing and Science Laboratory (PICSL) Department of Radiology University of Pennsylvania School of Medicine Philadelphia PA 19104 United States

Background: image registration is one of the most important and universally useful computational tasks in biomedical image analysis. A recent article by Xing & Qiu (IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10):2081-2092, 2011) is based on an inappropriately narrow conceptualization of the image registration problem as the task of making two images look alike, which disregards whether the established spatial correspondence is plausible. The authors propose a new algorithm, Nonparametric Local Smoothing (NLS) for image registration, but use image similarities alone as a measure of registration performance, although these measures do not relate reliably to the realism of the correspondence map. Results: Using data obtained from its authors, we show experimentally that the method proposed by Xing & Qiu is not an effective registration algorithm. While it optimizes image similarity, it does not compute accurate, interpretable transformations. Even judged by image similarity alone, the proposed method is consistently outperformed by a simple pixel permutation algorithm, which is known by design not to compute valid registrations. Conclusions: This study has demonstrated that the NLS algorithm proposed recently for image registration, and published in one of the most respected journals in computer science, is not, in fact, an effective registration method at all. Our results also emphasize the general need to apply registration evaluation criteria that are sensitive to whether correspondences are accurate and mappings between images are physically interpretable. These goals cannot be achieved by simply reporting image similarities. © 2012 Rohlfing and Avants;licensee BioMed Central Ltd.

关键词： Accuracy Correspondence image registration

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Enhanced generative adversarial network for 3D brain MRI super-resolution

Enhanced generative adversarial network for 3D brain MRI sup...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Jiancong Wang Yuhua Chen Yifan Wu Jianbo Shi James Gee Penn Image Computing and Science Laboratory University of Pennsylvania Philadelphia PA USA Department of Bioengineering University of California Los Angeles CA USA Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

Single image super-resolution (SISR) reconstruction for magnetic resonance imaging (MRI) has generated significant interest because of its potential to not only speed up imaging but to improve quantitative processing and analysis of available image data. Generative Adversarial Networks (GAN) have proven to perform well in image recovery tasks. In this work, we followed the GAN framework and developed a generator coupled with discriminator to tackle the task of 3D SISR on T1 brain MRI images. We developed a novel 3D memory-efficient residual-dense block generator (MRDG) that achieves state-of-the-art performance in terms of SSIM (Structural Similarity), PSNR (Peak Signal to Noise Ratio) and NRMSE (Normalized Root Mean Squared Error) metrics. We also designed a pyramid pooling discriminator (PPD) to recover details on different size scales simultaneously. Finally, we introduced model blending, a simple and computational efficient method to balance between image and texture quality in the final output, to the task of SISR on 3D images.

关键词： Three-dimensional displays Gallium nitride Training Generative adversarial networks Spatial resolution Magnetic resonance imaging

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NON-UNIFORM SMOOTHING IN HIPPOCAMPUS-SPECIFIC GROUP FMRI ANALYSIS

NON-UNIFORM SMOOTHING IN HIPPOCAMPUS-SPECIFIC GROUP FMRI ANA...

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IEEE International Symposium on Biomedical Imaging

作者： Paul A. Yushkevich John A. Detre James C. Gee Department of Radiology Penn Image Computing and Science Laboratory USA Center for Functional Neuroimaging Departments of Neurology and Radiology University of Pennsylvania Philadelphia PA USA

ISBN: (纸本)9781424406715;1424406714

A framework for generating group-level statistical maps of functional activation in the hippocampus is presented. It aims to maximize the sensitivity and specificity of hippocampal maps by using a deformable model to normalize the hippocampus across subjects and by using a non-uniform smoothing strategy to reduce the confounding effects of extrahippocampal activation. Results of applying this framework with different smoothing parameters are presented in the context of a visual scene encoding study. The significance reported by the method in the hippocampus at low levels of smoothing is greater than when whole-brain analysis is used, while at higher levels of smoothing, whole-brain analysis detects clusters of equally high significance around the hippocampus, but not directly in the hippocampus.

关键词： Smoothing methods Hippocampus Deformable models Magnetic resonance imaging Testing Radiology Solid modeling Kernel image segmentation image analysis

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Enhanced generative adversarial network for 3D brain MRI super-resolution

arXiv

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arXiv 2019年

作者： Wang, Jiancong Chen, Yuhua Wu, Yifan Shi, Jianbo Gee, James Penn Image Computing and Science Laboratory University of Pennsylvania PhiladelphiaPA19104 United States Department of Bioengineering University of California Los AngelesCA90095 United States Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA19104 United States

Single image super-resolution (SISR) reconstruction for magnetic resonance imaging (MRI) has generated significant interest because of its potential to not only speed up imaging but to improve quantitative processing and analysis of available image data. Generative Adversarial Networks (GAN) have proven to perform well in recovering image texture detail, and many variants have therefore been proposed for SISR. In this work, we develop an enhancement to tackle GAN-based 3D SISR by introducing a new residual-in-residual dense block (RRDG) generator that is both memory efficient and achieves state-of-the-art performance in terms of PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity) and NRMSE (Normalized Root Mean Squared Error) metrics. We also introduce a patch GAN discriminator with improved convergence behavior to better model brain image texture. We proposed a novel the anatomical fidelity evaluation of the results using a pre-trained brain parcellation network. Finally, these developments are combined through a simple and efficient method to balance between image and texture quality in the final output. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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