检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

180 篇 会议
73 册 图书

馆藏范围

250 篇 电子文献
6 种 纸本馆藏

日期分布

学科分类号

118 篇 理学
- 117 篇 数学
- 19 篇 生物学
- 16 篇 统计学（可授理学、...
- 1 篇 物理学
112 篇 医学
- 85 篇 特种医学
- 54 篇 临床医学
- 44 篇 医学技术（可授医学...
58 篇 工学
- 52 篇 计算机科学与技术...
- 2 篇 机械工程
- 2 篇 仪器科学与技术
- 2 篇 控制科学与工程
- 2 篇 软件工程
- 1 篇 建筑学
3 篇 艺术学
- 3 篇 设计学（可授艺术学...

主题

35 篇 visualization
22 篇 diffusion mri
15 篇 image processing...
14 篇 computer graphic...
12 篇 magnetic resonan...
11 篇 topology
10 篇 tractography
10 篇 diffusion
9 篇 geometry
8 篇 deep learning
8 篇 artificial intel...
6 篇 computational ma...
6 篇 computational sc...
6 篇 computer imaging...
5 篇 mathematical and...
5 篇 eye tracking
5 篇 tensors
5 篇 eye movements
4 篇 simulation and m...
4 篇 pattern recognit...

机构

5 篇 university of ka...
4 篇 univ pavia dept ...
4 篇 univ cote dazur ...
4 篇 northwestern pol...
4 篇 univ n carolina ...
4 篇 university colle...
4 篇 univ n carolina ...
3 篇 ucl ctr med imag...
3 篇 linköping univer...
3 篇 lawrence livermo...
3 篇 zuse institute b...
3 篇 university of ca...
3 篇 univ verona comp...
3 篇 inria sophia ant...
3 篇 univ n carolina ...
3 篇 wright state uni...
3 篇 the university o...
3 篇 univ n carolina ...
3 篇 ecole polytech f...
3 篇 therapanacea

作者

10 篇 deriche rachid
8 篇 yap pew-thian
8 篇 hans hagen
7 篇 hans-christian h...
7 篇 shen dinggang
6 篇 wassermann demia...
6 篇 enrico kaden
6 篇 chen geng
6 篇 bernd hamann
5 篇 jones derek k.
5 篇 hamish carr
5 篇 thompson paul m.
5 篇 fick rutger
5 篇 marco reisert
5 篇 pizzolato marco
5 篇 pascucci valerio
5 篇 yogesh rathi
4 篇 julien tierny
4 篇 thiran jean-phil...
4 篇 nir talia m.

语言

244 篇 英文
9 篇 中文

检索条件"丛书名=Mathematics and Visualization"

共 253 条记录，以下是101-110 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

An Efficient Finite Element Solution of the Generalised Bloch-Torrey Equation for Arbitrary Domains

引用

MICCAI Workshop on Computational Diffusion MRI

作者： Beltrachini, Leandro Taylor, Zeike A. Frangi, Alejandro F. Univ Sheffield CISTIB Ctr Computat Imaging & Simulat Technol Biomed Dept Elect & Elect Engn Pam Liversidge BldgMappin St Sheffield S1 3JD S Yorkshire England

ISBN: (纸本)9783319285887;9783319285863

Nuclear magnetic resonance (NMR) is an invaluable tool for investigating porous media. Its use allows to study pore size distributions, fiber tortuosity, and permeability as a function of the relaxation time, diffusivity, and flow. This information was shown to be important in many applications, such as medical diagnosis and materials science. A complete NMR analysis involves the solution of the Bloch-Torrey (BT) equation. However, solving this equation analytically becomes intractable for all but the simplest geometries. We present an efficient numerical framework for solving the generalised BT equation. This method allows to obtain computational simulations of the NMR experiment in arbitrarily complex domains. In addition to the standard BT equation, the generalised BT formulation takes into account the flow and relaxation terms, allowing a better representation of the phenomena under scope. This framework is flexible enough to deal parametrically with any order of convergence in the spatial domain. Moreover, we developed a second-order implicit scheme for the temporal discretisation with similar computational demands as the existing explicit methods. This represents a huge step forward for obtaining reliable results with few iterations. Comparisons with analytical solutions and real data show the flexibility and accuracy of the proposed method.

关键词： Pore size

来源：评论

学校读者我要写书评

暂无评论

Multi-Tensor MAPMRI: How to Estimate Microstructural Information from Crossing Fibers

引用

MICCAI Workshop on Computational Diffusion MRI

作者： Zucchelli, Mauro Brusini, Lorenza Mendez, C. Andres Menegaz, Gloria Univ Verona Verona Italy

ISBN: (纸本)9783319285887;9783319285863

Diffusion Magnetic Resonance Imaging (dMRI) is able to detect the properties of tissue microstructure underneath the voxel through the imaging of water molecules diffusion. Many reconstruction methods have been proposed to calculate the Orientation Distribution Function (ODF) from the diffusion signal in order to distinguish between coherent fiber bundles and crossing fibers. The diffusion signal was also used to infer other microstructural information such as the axon diameter, but most often in areas with coherent fiber direction such as the corpus callosum. In this work, we developed a reconstruction model called Multi-Tensor MAPMRI (MT-MAPMRI) that is an extension of theMAPMRI model which improves the performance of MAPMRI for crossing fibers. In particular, it provides (a) enhanced signal fitting;(b) improved ODFs;(c) a more accurate diameter estimation. The model was tested and validated on both simulated and in-vivo data.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Registration Strategies for Whole-Body Diffusion-Weighted MRI Stitching

引用

MICCAI Workshop on Computational Diffusion MRI

作者： Ceranka, Jakub Polfliet, Mathias Lecouvet, Frederic Michoux, Nicolas de Mey, Johan Vandemeulebroucke, Jef Vrije Univ Brussel Dept Elect & Informat ETRO Pl Laan 2 B-1050 Brussels Belgium iMinds Dept Med IT Gaston Crommenlaan 8Box 102 B-9050 Ghent Belgium Clin Univ St Luc Dept Radiol Hippocrate Ave 10-2942 Brussels Belgium Univ Ziekenhuis Brussel UZ Brussel Dept Radiol Laarbeeklaan 101 B-1090 Brussels Belgium

ISBN: (纸本)9783319285887;9783319285863

With the development of ultra-fast magnetic resonance imaging sequences, whole-body diffusion-weighted magnetic resonance imaging (WB-DWI) becomes a popular diagnostic tool in patient cancer screening. Modality can improve plenty of clinical investigations such as lymphoma, multiple melanoma or metastatic bone cancer diagnosis. Because of vast body coverage and MR scanner limitations, whole-body image is acquired in blocks, called stations. Precise 'stitching' of whole-body stations is essential to ensure correct image formation, yet there are not many commercially available registration algorithms. We developed and investigated several registration methods based on apparent diffusion coefficient (ADC) and diffusion-weighted images (DWI) to improve station-to-station registration and WB-DWI image quality. This paper reports on registration results of 52 whole-body DWI images and compares them with other already existing methods. Proposed registration techniques based on ADC images demonstrated superior performance over other registration methods.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Estimation of Fiber Orientations Using Neighborhood Information

引用

MICCAI Workshop on Computational Diffusion MRI

作者： Ye, Chuyang Zhuo, Jiachen Gullapalli, Rao P. Prince, Jerry L. Chinese Acad Sci Brainnetome Ctr Inst Automat Beijing Peoples R China Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA Univ Maryland Sch Med Dept Radiol Baltimore MD 21201 USA

ISBN: (纸本)9783319285887;9783319285863

Diffusion magnetic resonance imaging (dMRI) has been used to noninvasively reconstruct fiber tracts. Fiber orientation (FO) estimation is a crucial step in the reconstruction, especially in the case of crossing fibers. In FO estimation, it is important to incorporate spatial coherence of FOs to reduce the effect of noise. In this work, we propose a method of FO estimation using neighborhood information. The diffusion signal is modeled by a fixed tensor basis. The spatial coherence is enforced in weighted l(1)-norm regularization terms, which contain the interaction of directional information between neighbor voxels. Data fidelity is ensured by the agreement between raw and reconstructed diffusion signals. The resulting objective function is solved using a block coordinate descent algorithm. Experiments were performed on a digital crossing phantom, ex vivo tongue dMRI data, and in vivo brain dMRI data for qualitative and quantitative evaluation. The results demonstrate that the proposed method improves the quality of FO estimation.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Super-Resolution Reconstruction of Diffusion-Weighted Images Using 4D Low-Rank and Total Variation

引用

MICCAI Workshop on Computational Diffusion MRI

作者： Shi, Feng Cheng, Jian Wang, Li Yap, Pew-Thian Shen, Dinggang Univ North Carolina Chapel Hill Dept Radiol Chapel Hill NC 27514 USA Univ North Carolina Chapel Hill BRIC Chapel Hill NC 27514 USA NICHHD Sect Tissue Biophys & Biomimet PPITS NIH Bethesda MD 20892 USA

ISBN: (纸本)9783319285887;9783319285863

Diffusion-weighted imaging (DWI) provides invaluable information in white matter microstructure and is widely applied in neurological applications. However, DWI is largely limited by its relatively low spatial resolution. In this paper, we propose an image post-processing method, referred to as super-resolution reconstruction, to estimate a high spatial resolution DWI from the input low-resolution DWI, e.g., at a factor of 2. Instead of requiring specially designed DWI acquisition of multiple shifted or orthogonal scans, our method needs only a single DWI scan. To do that, we propose to model both the blurring and downsampling effects in the image degradation process where the low-resolution image is observed from the latent high-resolution image, and recover the latent high-resolution image with the help of two regularizations. The first regularization is four-dimensional (4D) low-rank, proposed to gather self-similarity information from both the spatial domain and the diffusion domain of 4D DWI. The second regularization is total variation, proposed to depress noise and preserve local structures such as edges in the image recovery process. Extensive experiments were performed on 20 subjects, and results show that the proposed method is able to recover the fine details of white matter structures, and outperform other approaches such as interpolation methods, non-local means based upsampling, and total variation based upsampling.

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

White Matter Fiber Set Simplification by Redundancy Reduction with Minimum Anatomical Information Loss

引用

MICCAI Workshop on Computational Diffusion MRI

作者： Moreno, Gali Zimmerman Alexandroni, Guy Greenspan, Hayit Tel Aviv Univ Dept Biomed Engn IL-69978 Tel Aviv Israel Tel Aviv Univ Dept Elect Engn IL-69978 Tel Aviv Israel

ISBN: (纸本)9783319285887;9783319285863

Advanced Diffusion Weighted Imaging (DWI) techniques and leading tractography algorithms produce dense fiber sets of hundreds of thousands of fibers, or more. In order to make fiber based analysis more practical, the fiber set needs to be preprocessed to eliminate redundancies and to keep only essential representative fibers. In this paper we evaluate seven commonly used distance metrics for fiber clustering and present a novel approach for comparing the metrics as well as estimating the anatomical information loss as a function of the reduction rate. The framework includes pre-clustering into sub-groups using K-means, followed by further decomposition using Hierarchical Clustering, each time with a different distance metric. Finally, volume histograms comparison is used to compare the reduction quality with the different metrics. The proposed comparison was applied to a dataset containing tractographies of four healthy individuals. Each set contains around 600k fibers.

关键词： Fibers

来源：评论

学校读者我要写书评

暂无评论

Brain Tissue Micro-Structure Imaging from Diffusion MRI Using Least Squares Variable Separation

引用

MICCAI Workshop on Computational Diffusion MRI

作者： Farooq, Hamza Xu, Junqian Yacoub, Essa Georgiou, Tryphon Lenglet, Christophe Univ Minnesota Dept Elect & Comp Engn Minneapolis MN USA Icahn Sch Med Mt Sinai Dept Radiol Translat & Mol Imaging Inst New York NY 10029 USA Univ Minnesota Dept Radiol Ctr Magnet Resonance Res Minneapolis MN 55455 USA

ISBN: (纸本)9783319285887;9783319285863

We introduce a novel data fitting procedure of multi compartmentmodels of the brain white matter for diffusion MRI (dMRI) data. These biophysical models aim to characterize important micro-structure quantities like axonal radius, density and orientations. In order to describe the underlying tissue properties, a variety of models for intra-/extra-axonal diffusion signals have been proposed. Combinations of these analytic models are used to predict the diffusion MRI signal in multi-compartment settings. However, parameter estimation from these multi-compartment models is an ill-posed problem. Consequently, many existing fitting algorithms either rely on an initial grid search to find a good start point, or have strong assumptions like single fiber orientation to estimate some of these parameters from simpler models like the diffusion tensor (DT). In both cases, there is a tradeoff between computational complexity and accuracy of the estimated parameters. Here, we describe a novel algorithm based on the separation of the Nonlinear Least Squares (NLLS) fitting problem, via Variable Projection Method, to search for nonlinearly and linearly entering parameters independently. We use stochastic global search algorithms to find a global minimum, while estimating non-linearly entering parameters. The approach is independent of any starting point, and does not rely on estimates from simpler models. We show that the suggested algorithm is faster than algorithms involving grid search, and its greater accuracy and robustness are demonstrated on synthetic as well as ex-/in-vivo data.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Using Automatic HARDI Feature Selection, Registration, and Atlas Building to Characterize the Neuroanatomy of Aβ Pathology

引用

MICCAI Workshop on Computational Diffusion MRI

作者： Schwab, Evan Yassa, Michael A. Weiner, Michael Vidal, Rene Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA Univ Calif Irvine Dept Neurobiol & Behav Irvine CA USA Univ Calif San Francisco Dept Radiol San Francisco CA USA

ISBN: (纸本)9783319285887;9783319285863

The detection of white matter microstructural changes using diffusion magnetic resonance imaging (dMRI) often involves extracting a small set of scalar features, such as fractional anisotropy (FA) and mean diffusivity (MD) in diffusion tensor imaging (DTI). With the advent of more advanced dMRI techniques, such as high angular resolution diffusion imaging (HARDI), a number of mathematically inspired new scalar features have been proposed. However, it is unclear how to select the most biologically informative combinations of features for biomarker discovery. This paper proposes an automatic HARDI feature selection algorithm which is based on registering HARDI features to feature atlases for optimal clinical usability in population studies. We apply our framework to the characterization of beta-amyloid (A beta) pathology for the early detection of Alzheimer's disease (AD) to better understand the relationship between A beta pathology and degenerative changes in neuroanatomy.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Reliability of Structural Connectivity Examined with Four Different Diffusion Reconstruction Methods at Two Different Spatial and Angular Resolutions

引用

MICCAI Workshop on Computational Diffusion MRI

作者： Villalon-Reina, J. E. Nir, T. M. Zhan, L. McMahon, K. L. de Zubicaray, G. I. Wright, M. J. Jahanshad, N. Thompson, P. M. Univ Southern Calif Imaging Genet Ctr USC Stevens Neuroimaging & Informat Inst Marina Del Rey CA 90292 USA Univ Queensland Ctr Adv Imaging Brisbane Qld Australia Univ Queensland Sch Psychol Brisbane Qld Australia QIMR Berghofer Med Res Inst Brisbane Qld Australia

ISBN: (纸本)9783319285887;9783319285863

Diffusion magnetic resonance imaging (dMRI) has had a great impact on the study of the human brain connectome. Tractography methods allow for the reconstruction of white matter fiber tracts and bundles across the brain, by tracing the estimated direction of water diffusion across neighboring voxels. The tracts can then be used in conjunction with cortical parcellations to create structural connectivity matrices, to map the pattern and distribution of connections between cortical regions. However, the reliability of connectivity matrices is unclear. Tractography results depend on image resolution, and some reconstruction methods used to resolve the voxel-wise microstructure may be more robust to changes in resolution than others, leading to more stable connectivity estimates. We examined the reliability of structural connectivity matrices in 20 healthy young adults imaged with both high and low-resolution dMRI at two time points. We found that the Constrained Spherical Deconvolution (CSD) model produces the most reliable connections for both lower resolution and high resolution scans.

关键词： Reliability

来源：评论

学校读者我要写书评

暂无评论

Holistic Image Reconstruction for Diffusion MRI

引用

MICCAI Workshop on Computational Diffusion MRI

作者： Golkov, Vladimir Portegies, Jorg M. Golkov, Antonij Duits, Remco Cremers, Daniel Tech Univ Munich Dept Informat Garching Germany Eindhoven Univ Technol Dept Math & Comp Sci Eindhoven Netherlands Univ Augsburg Dept Math Augsburg Germany Eindhoven Univ Technol Dept Biomed Engn Eindhoven Netherlands

ISBN: (纸本)9783319285887;9783319285863

Diffusion MRI provides unique information on the microarchitecture of biological tissues. One of the major challenges is finding a balance between image resolution, acquisition duration, noise level and image artifacts. Recent methods tackle this challenge by performing super-resolution reconstruction in image space or in diffusion space, regularization of the image data or of postprocessed data (such as the orientation distribution function, ODF) along different dimensions, and/or impose data-consistency in the original acquisition space. Each of these techniques has its own advantages;however, it is rare that even a few of them are combined. Here we present a holistic framework for diffusion MRI reconstruction that allows combining the advantages of all these techniques in a single reconstruction step. In proof-of-concept experiments, we demonstrate super-resolution on HARDI shells and in image space, regularization of the ODF and of the images in spatial and angular dimensions, and data consistency in the original acquisition space. Reconstruction quality is superior to standard reconstruction, demonstrating the feasibility of combining advanced techniques into one step.

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共26页 << < 7 8 9 10 11 12 13 14 15 16 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：