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: 6949109
• 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/6949109
• 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）：功能性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，这些工具受到固有的低信噪比的数据，并从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，现在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。该项目的重点是优化算法的估计和去除噪声的设计，对现有的数据驱动方法的适用性的理解，并在fMRI数据的新的盲源分离方法的发展。特别注意将给予新提出的方法通过模拟数据集和专门设计的功能磁共振成像实验提供的收益的量化。第一个具体的目标是使用时空四维多分辨率分析来定义一个“理想的去噪”计划为给定的研究。它将广泛使用最佳小波包基的概念，它允许信号的最有效的表示。这个概念将首先在fMRI静息数据集上进行验证，然后在模拟和实际数据上测量其效率。第二个具体的目标集中在盲源分离方法。独立成分分析将进行深入的研究，以精确地定义其适用于功能磁共振成像数据的领域。通过使用稀疏性与时频方法，我们将开发新的源分离算法，并将证明其对模拟和真实的数据的鲁棒性。