New Wavelet-based and Source Separation Methods for fMRI

新的基于小波和源分离的功能磁共振成像方法

• 批准号: 6663283
• 负责人: INGRID DAUBECHIES
• 金额: $ 39.5万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-19 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6663283
• 关键词: artificial intelligence; bioimaging /biomedical imaging; brain imaging /visualization /scanning; clinical research; computer data analysis; computer program /software; computer system design /evaluation; functional magnetic resonance imaging; human subject; mathematics; phantom model; technology /technique development

DESCRIPTION (provided by applicant): Available methods of analysis for functional Magnetic Resonance Imaging offer a wealth of possibilities to researchers using this neuroimaging modality. However, these tools suffer from the inherent low signal to noise ratio of the data, and from the limitations of widely used model-based approaches. These problems have been addressed by the community and the literature now describes numerous methods that can remove part of the noise and extract brain activity pattern in a data-driven fashion. This project focuses on the design of optimized algorithms for the estimation and removal of the noise, on the understanding of the applicability of existing data-driven approaches, and on the development of new blind source separation methods for fMRI data. Particular attention will be given to quantification of the gains provided by the newly proposed methods by working on simulated datasets and specifically designed fMRI experiments. The first specific aim is to use a spatio-temporal four-dimensional multiresolution analysis to define an "'ideal denoising" scheme for a given study. It will make extensive use of the concept of best wavelet packet basis, which allows the most efficient representation of a signal. The concept wilt first be validated on fMRI rest datasets, and its efficiency will then be measured on simulated and actual data. The second specific aim focuses on blind source separation methods. An in depth study of Independent Component Analysis will be carried out to precisely define its field of applicability on fMRI data. By using sparsity together with time-frequency methods, we will develop new source separation algorithms and will demonstrate their robustness on both simulated and real data.

描述(由申请人提供)：功能磁共振成像的可用分析方法为使用这种神经成像模式的研究人员提供了丰富的可能性。然而，这些工具存在数据固有的低信噪比，以及广泛使用的基于模型的方法的局限性。这些问题已经被社区解决了，现在的文献描述了许多方法，可以去除部分噪声，并以数据驱动的方式提取大脑活动模式。本项目的重点是设计用于估计和去除噪声的优化算法，了解现有数据驱动方法的适用性，并开发新的功能磁共振数据盲源分离方法。将特别注意通过模拟数据集和专门设计的功能磁共振实验来量化新提出的方法所提供的增益。第一个具体目标是使用时空四维多分辨率分析来为给定的研究定义“理想的去噪”方案。它将广泛使用最佳小波包基的概念，它允许最有效地表示信号。这一概念将首先在fMRI REST数据集上得到验证，然后将在模拟和实际数据上测量其效率。第二个具体目标是盲源分离方法。我们将对独立成分分析进行深入的研究，以明确其在fMRI数据上的适用范围。通过将稀疏性和时频方法相结合，我们将开发新的源分离算法，并将在模拟数据和真实数据上展示它们的鲁棒性。