权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Automated Analysis of Movement Disorders from Diffusion and Functional MRI

通过弥散和功能 MRI 自动分析运动障碍

基本信息

批准号：
1724174
负责人：
Baba Vemuri
金额：
$ 106万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724174&HistoricalAwards=false
关键词：
Automated Analysis Movement Disorders Diffusion

项目摘要

Magnetic resonance imaging (MRI) is the most widely used diagnostic imaging tool for detecting neurodegenerative disorders such as Parkinson's Disease. This project will develop new automated methods for detecting subtle effects that can be revealed by MRI, including changes in water diffusional properties of human brain tissue, and functional brain activity. To assess the deviation from the normal brains, a computationally efficient algorithm will be developed to construct a population-specific brain structural template from a normal brain population. Further, a new algorithm will be developed to facilitate the detection of Parkinson's using diffusion MRI data. Finally, novel algorithms for establishing the correlation between the information derived from diffusion and functional MRI data will be developed, enabling prediction of functional activity given the anatomical information and vice-versa. Inferring such a correlation will make it possible to predict functional changes due to changes in tissue microstructure caused by neurodegenerative disorders and vice-versa.In summary, the precise project goals are: (i) To develop a computationally efficient template brain map construction algorithm for features derived from diffusion MRI. In this context, the ensemble average propagator (EAP), which captures both orientation and shape information of the diffusion process at each voxel in the diffusion MRI data, is proposed. Validation of the constructed template will be performed using standard evaluation metrics for template-based segmentation. (ii) To develop novel methods to automatically discriminate between control and Parkinson's groups using the EAP fields as well as Cauchy deformation tensors (that capture the changes in EAP fields). Validation of the classifier will be achieved using the standard leave-k-out strategy. (iii) To develop a novel algorithm for kernel-based nonlinear regression between EAP fields derived from diffusion MRI and scalar-valued fields derived from functional MRI activation maps. The algorithm will be able to predict the level of activation given the EAP fields and vice-versa. These predictions will be validated using a priori labeled data sets. Predicting functional responses from structural information and vice-versa will significantly impact treatment planning of patients with Parkinson's Disease and other neurodegenerative disorders. The multidisciplinary nature of this project will provide the opportunity to collectively train graduate students from diverse backgrounds in the STEM related fields of this project.

磁共振成像 (MRI) 是用于检测帕金森病等神经退行性疾病的最广泛使用的诊断成像工具。该项目将开发新的自动化方法来检测 MRI 可以揭示的微妙影响，包括人脑组织水扩散特性的变化和功能性大脑活动。为了评估与正常大脑的偏差，将开发一种计算高效的算法，从正常大脑群体构建特定群体的大脑结构模板。此外，还将开发一种新算法，以促进利用扩散 MRI 数据检测帕金森病。最后，将开发用于建立源自扩散的信息和功能性 MRI 数据之间的相关性的新算法，从而能够根据解剖信息预测功能活动，反之亦然。推断这种相关性将使得预测由于神经退行性疾病引起的组织微观结构变化而导致的功能变化成为可能，反之亦然。总之，精确的项目目标是：（i）为源自扩散 MRI 的特征开发计算高效的模板脑图构建算法。在这种情况下，提出了集合平均传播器（EAP），它可以捕获扩散 MRI 数据中每个体素的扩散过程的方向和形状信息。将使用基于模板的分割的标准评估指标对构建的模板进行验证。 (ii) 开发新方法，使用 EAP 场以及柯西变形张量（捕获 EAP 场的变化）自动区分对照组和帕金森氏症组。分类器的验证将使用标准的 left-k-out 策略来实现。 (iii) 开发一种新的算法，用于在源自扩散 MRI 的 EAP 场和源自功能 MRI 激活图的标量值场之间进行基于内核的非线性回归。该算法将能够根据 EAP 字段预测激活级别，反之亦然。这些预测将使用先验标记数据集进行验证。根据结构信息预测功能反应（反之亦然）将显着影响帕金森病和其他神经退行性疾病患者的治疗计划。该项目的多学科性质将为该项目的 STEM 相关领域共同培养来自不同背景的研究生提供机会。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Sparse Exact PGA on Riemannian Manifolds

黎曼流形上的稀疏精确 PGA

DOI：
10.1109/iccv.2017.536
发表时间：
2017
期刊：
IEEE International Conference on Computer Vision
影响因子：
0
作者：
Banerjee, Monami;Chakraborty, Rudrasis;Vemuri, Baba C.
通讯作者：
Vemuri, Baba C.

A Higher Order Manifold-Valued Convolutional Neural Network with Applications to Diffusion MRI Processing

高阶流形值卷积神经网络及其在扩散 MRI 处理中的应用

DOI：
10.1007/978-3-030-78191-0
发表时间：
2021
期刊：
International Conference on Information Processing in Medical Imaging (IPMI
影响因子：
0
作者：
Bouza, J;Yang, CH;Vaillancourt, D;Vemuri, BC
通讯作者：
Vemuri, BC

VolterraNet: A Higher Order Convolutional Network With Group Equivariance for Homogeneous Manifolds

VolterraNet：具有同质流形群等方差的高阶卷积网络

DOI：
10.1109/tpami.2020.3035130
发表时间：
2022
期刊：
IEEE Transactions on Pattern Analysis and Machine Intelligence
影响因子：
23.6
作者：
Banerjee, Monami;Chakraborty, Rudrasis;Bouza, Jose;Vemuri, Baba C.
通讯作者：
Vemuri, Baba C.

Exploiting structural redundancy in q-space for improved EAP reconstruction from highly undersampled (k, q)-space in DMRI

DOI：
10.1016/j.media.2019.02.014
发表时间：
2019-05-01
期刊：
MEDICAL IMAGE ANALYSIS
影响因子：
10.9
作者：
Sun，Jiaqi;Entezari，Alireza;Vemuri，C.
通讯作者：
Vemuri，C.

A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices

对称正定矩阵流形的统计递归模型

DOI：
发表时间：
2018
期刊：
Advances in neural information processing systems
影响因子：
0
作者：
Chakraborty, R;Yang, CH;Zhen, X;Banerjee, M;Archer, D;Vaillancourt, D;Singh, V;Vemuri, B.C.
通讯作者：
Vemuri, B.C.